In previous studies, we developed an empirical account of user engagement with software agents. We
formalized this model, tested it for internal consistency, and implemented it into a series of software agents to
have them build up an affective relationship with their users. In addition, we equipped the agents with a module
for affective decision-making, as well as the capability to generate a series of emotions (e.g., joy and anger). As
follow-up of a successful pilot study with real users, the current paper employs a non-naïve version of a Turing
Test to compare an agent’s affective performance with that of a human. We compared the performance of an
agent equipped with our cognitive model to the performance of a human that controlled the agent in a Wizard
of Oz condition during a speed-dating experiment in which participants were told they were dealing with a
robot in bot h conditions. Participants did not detect any differences between the two conditions in the
emotions the agent experienced and in the way he supposedly perceived the participants. As is, our model can
be used for designing believable virtual agents or humanoid robots on the surface level of emotion expression.
Paint With Me: Stimulating Creativity and Empathy IEEE VR 2017 PresentationLynda Joy Gerry
Presentation of TGCV paper Paint With Me: Stimulating Creativity and Empathy While Painting with a Painter in Virtual Reality on March 22, 2017 at IEEE VR 2017 in Los Angeles.
Paint With Me: Stimulating Creativity and Empathy IEEE VR 2017 PresentationLynda Joy Gerry
Presentation of TGCV paper Paint With Me: Stimulating Creativity and Empathy While Painting with a Painter in Virtual Reality on March 22, 2017 at IEEE VR 2017 in Los Angeles.
Learning Process Interaction Aided by an Adapter Agentpaperpublications3
Abstract: Computational models have played an important role in the discovery and understanding of the complexities during the learning process. One complexity is the distraction factor on educator-learner interaction affecting the quality of the learning process.
We model an adaptive system model able to dynamically adapt considering user’s performance, simulating the learner as a museum user and the educator as an exhibition module using BDI agents; we adapt the BDI architecture using Type-2 fuzzy inference system to add perceptual human-like capability on agents in order to describe the interaction on user's experience. The resulting model allows content adaptation by creating a personalized interaction environment.
Emotion-oriented computing: Possible uses and applicationsAndré Valdestilhas
This article discusses the concepts of using digital television affective computing and computer vision.
The proposal involves the union of some techniques such as capturing facial expressions through a video
camera, use of accelerometers in ball and touch holograms to work a certain level of interactivity with the
viewer. Some uses of the proposal in question are described, such as control of the hearing, background
content, among others. This article reveals numerous benefits that can be addressed with the use of
matters presented which can be applied in a broad context, such as for the blind in video games, among
others
How to deal with difficult people - Timothy DimoffCase IQ
If your job involves communicating with employees under difficult circumstances, you have probably encountered aggressive or uncooperative people. Handling these situations competently can help you get the results you need rather than an ugly confrontation. Join i-Sight and Timothy Dimoff for a free one-hour webinar: How to Deal with Difficult People.
During this webinar you will learn;
Aggressive versus assertive behavior
The difference between reacting and responding
Stages of aggression
De-escalating aggression
Things never to say to someone
How to speak “Peace Language”
Het belang van privacy, als je niets te verbergen hebt Matthijs Pontier
1984, amsterdam, amsterdam-west, anpr, cameratoezicht, democratie, function creep, gemeenteraad, huxley, marcouch, orwell, panopticon, piratenpartij, politiek, ppnl, preventief fouilleren, privacy, surveillance, veiligheid, waterbedeffect
1. Het belang van privacy als je niets te verbergen hebt Dr. Matthijs Pontier
2. Privacy inperken voor veiligheid? 1. Niet effectief 2. Leidt tot ongezonde machtsverhouding 3. Privacy juist voorwaarde voor veiligheid 4. Er bestaan prima alternatieven
3. Niets te verbergen? • Iedereen heeft wel wat te verbergen • Functie in sociale relaties • Als je niks te verbergen hebt, mogen anderen dan ook niks te verbergen hebben? • Snowden: “Ik vind privacy niet belangrijk, want ik heb toch niets te verbergen”, is als “Ik vind vrijheid meningsuiting niet belangrijk, want ik heb toch niets te melden”
4. Ron Kowsoleea
5. Scheve Machtsverhouding Burger - Overheid – Reisgedrag en function creep – Bespieden telefoon- en computergebruik – Internet of Things Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
6. Vrije samenleving nodig voor vooruitgang • We weten niet wat waar wanneer fout is Zelfcensuur • Met huidige controlesystemen was homoseksualiteit nog steeds illegaal geweest Dr. Matthijs Pontier, UUMUN TEDx
7. Overheid niet altijd betrouwbaar • Overheid laat gegevens slingeren • WRR: Overheid onbetrouwbaar met gegevens. Informatie wordt gemanipuleerd. • Ook overheid kan misdadig gedrag vertonen
8. Sleepnetmethode + Profiling • Discriminatie zonder te weten waarop • Speuren naar ‘afwijkend gedrag’ Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
9. Slachtoffers van Profiling
13. Slachtoffers van Profiling: Daniel
14. Corporate surveillance: ‘Smart TV’ Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
15. Smart TV hackers are filming couples having sex on their sofa’s and they are putting it on porn sites! Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
16. Smart technology • Als technologie voor je denkt bepalen programmeurs dan wat je denkt? Dr. Matthijs Pontier, Piratenpartij – HvA, HBO Rechten, 2-9-2016
17. Wanneer algoritmes ons controleren... Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
19. <slide> Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
20. • Enrypt e-mail Protonmail • Encrypt phone / messaging Signal • Gebruik adblockers en DNT uBlock / DNT • Iets van het Internet afhalen bestaat niet Privacy: Oplossingen
21. Veilig en vrij • Privacy juist voorwaarde voor veiligheid • Herstel machtsverhouding burger overheid • Kies voor effectieve maatregelen – Gerichte surveillance
22. Bedankt! • @Matthijs85 • Matthijs Pontier • matthijs@piratenpartij.nl • http://www.piratenpartij.nl/ • @Piratenpartij • 15/16 oktober houden we ons congres met sprekers, kunst en muziek •
The study of attention estimation for child-robot interaction scenariosjournalBEEI
One of the biggest challenges in human-agent interaction (HAI) is the development of an agent such as a robot that can understand its partner (a human) and interact naturally. To realize this, a system (agent) should be able to observe a human well and estimate his/her mental state. Towards this goal, in this paper, we present a method of estimating a child's attention, one of the more important human mental states, in a free-play scenario of child-robot interaction (CRI). To realize attention estimation in such CRI scenario, first, we developed a system that could sense a child's verbal and non-verbal multimodal signals such as gaze, facial expression, proximity, and so on. Then, the observed information was used to train a model that is based on a Support Vector Machine (SVM) to estimate a human's attention level. We investigated the accuracy of the proposed method by comparing with a human judge's estimation, and obtained some promising results which we discuss here.
Learning Process Interaction Aided by an Adapter Agentpaperpublications3
Abstract: Computational models have played an important role in the discovery and understanding of the complexities during the learning process. One complexity is the distraction factor on educator-learner interaction affecting the quality of the learning process.
We model an adaptive system model able to dynamically adapt considering user’s performance, simulating the learner as a museum user and the educator as an exhibition module using BDI agents; we adapt the BDI architecture using Type-2 fuzzy inference system to add perceptual human-like capability on agents in order to describe the interaction on user's experience. The resulting model allows content adaptation by creating a personalized interaction environment.
Emotion-oriented computing: Possible uses and applicationsAndré Valdestilhas
This article discusses the concepts of using digital television affective computing and computer vision.
The proposal involves the union of some techniques such as capturing facial expressions through a video
camera, use of accelerometers in ball and touch holograms to work a certain level of interactivity with the
viewer. Some uses of the proposal in question are described, such as control of the hearing, background
content, among others. This article reveals numerous benefits that can be addressed with the use of
matters presented which can be applied in a broad context, such as for the blind in video games, among
others
How to deal with difficult people - Timothy DimoffCase IQ
If your job involves communicating with employees under difficult circumstances, you have probably encountered aggressive or uncooperative people. Handling these situations competently can help you get the results you need rather than an ugly confrontation. Join i-Sight and Timothy Dimoff for a free one-hour webinar: How to Deal with Difficult People.
During this webinar you will learn;
Aggressive versus assertive behavior
The difference between reacting and responding
Stages of aggression
De-escalating aggression
Things never to say to someone
How to speak “Peace Language”
Het belang van privacy, als je niets te verbergen hebt Matthijs Pontier
1984, amsterdam, amsterdam-west, anpr, cameratoezicht, democratie, function creep, gemeenteraad, huxley, marcouch, orwell, panopticon, piratenpartij, politiek, ppnl, preventief fouilleren, privacy, surveillance, veiligheid, waterbedeffect
1. Het belang van privacy als je niets te verbergen hebt Dr. Matthijs Pontier
2. Privacy inperken voor veiligheid? 1. Niet effectief 2. Leidt tot ongezonde machtsverhouding 3. Privacy juist voorwaarde voor veiligheid 4. Er bestaan prima alternatieven
3. Niets te verbergen? • Iedereen heeft wel wat te verbergen • Functie in sociale relaties • Als je niks te verbergen hebt, mogen anderen dan ook niks te verbergen hebben? • Snowden: “Ik vind privacy niet belangrijk, want ik heb toch niets te verbergen”, is als “Ik vind vrijheid meningsuiting niet belangrijk, want ik heb toch niets te melden”
4. Ron Kowsoleea
5. Scheve Machtsverhouding Burger - Overheid – Reisgedrag en function creep – Bespieden telefoon- en computergebruik – Internet of Things Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
6. Vrije samenleving nodig voor vooruitgang • We weten niet wat waar wanneer fout is Zelfcensuur • Met huidige controlesystemen was homoseksualiteit nog steeds illegaal geweest Dr. Matthijs Pontier, UUMUN TEDx
7. Overheid niet altijd betrouwbaar • Overheid laat gegevens slingeren • WRR: Overheid onbetrouwbaar met gegevens. Informatie wordt gemanipuleerd. • Ook overheid kan misdadig gedrag vertonen
8. Sleepnetmethode + Profiling • Discriminatie zonder te weten waarop • Speuren naar ‘afwijkend gedrag’ Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
9. Slachtoffers van Profiling
13. Slachtoffers van Profiling: Daniel
14. Corporate surveillance: ‘Smart TV’ Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
15. Smart TV hackers are filming couples having sex on their sofa’s and they are putting it on porn sites! Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
16. Smart technology • Als technologie voor je denkt bepalen programmeurs dan wat je denkt? Dr. Matthijs Pontier, Piratenpartij – HvA, HBO Rechten, 2-9-2016
17. Wanneer algoritmes ons controleren... Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
19. <slide> Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
20. • Enrypt e-mail Protonmail • Encrypt phone / messaging Signal • Gebruik adblockers en DNT uBlock / DNT • Iets van het Internet afhalen bestaat niet Privacy: Oplossingen
21. Veilig en vrij • Privacy juist voorwaarde voor veiligheid • Herstel machtsverhouding burger overheid • Kies voor effectieve maatregelen – Gerichte surveillance
22. Bedankt! • @Matthijs85 • Matthijs Pontier • matthijs@piratenpartij.nl • http://www.piratenpartij.nl/ • @Piratenpartij • 15/16 oktober houden we ons congres met sprekers, kunst en muziek •
The study of attention estimation for child-robot interaction scenariosjournalBEEI
One of the biggest challenges in human-agent interaction (HAI) is the development of an agent such as a robot that can understand its partner (a human) and interact naturally. To realize this, a system (agent) should be able to observe a human well and estimate his/her mental state. Towards this goal, in this paper, we present a method of estimating a child's attention, one of the more important human mental states, in a free-play scenario of child-robot interaction (CRI). To realize attention estimation in such CRI scenario, first, we developed a system that could sense a child's verbal and non-verbal multimodal signals such as gaze, facial expression, proximity, and so on. Then, the observed information was used to train a model that is based on a Support Vector Machine (SVM) to estimate a human's attention level. We investigated the accuracy of the proposed method by comparing with a human judge's estimation, and obtained some promising results which we discuss here.
Computer Science
Active and Programmable Networks
Active safety systems
Ad Hoc & Sensor Network
Ad hoc networks for pervasive communications
Adaptive, autonomic and context-aware computing
Advance Computing technology and their application
Advanced Computing Architectures and New Programming Models
Advanced control and measurement
Aeronautical Engineering,
Agent-based middleware
Alert applications
Automotive, marine and aero-space control and all other control applications
Autonomic and self-managing middleware
Autonomous vehicle
Biochemistry
Bioinformatics
BioTechnology(Chemistry, Mathematics, Statistics, Geology)
Broadband and intelligent networks
Broadband wireless technologies
CAD/CAM/CAT/CIM
Call admission and flow/congestion control
Capacity planning and dimensioning
Changing Access to Patient Information
Channel capacity modelling and analysis
Civil Engineering,
Cloud Computing and Applications
Collaborative applications
Communication application
Communication architectures for pervasive computing
Communication systems
Computational intelligence
Computer and microprocessor-based control
Computer Architecture and Embedded Systems
Computer Business
Computer Sciences and Applications
Computer Vision
Computer-based information systems in health care
Computing Ethics
Computing Practices & Applications
Congestion and/or Flow Control
Content Distribution
Context-awareness and middleware
Creativity in Internet management and retailing
Cross-layer design and Physical layer based issue
Cryptography
Data Base Management
Data fusion
Data Mining
Data retrieval
Data Storage Management
Decision analysis methods
Decision making
Digital Economy and Digital Divide
Digital signal processing theory
Distributed Sensor Networks
Drives automation
Drug Design,
Drug Development
DSP implementation
E-Business
E-Commerce
E-Government
Electronic transceiver device for Retail Marketing Industries
Electronics Engineering,
Embeded Computer System
Emerging advances in business and its applications
Emerging signal processing areas
Enabling technologies for pervasive systems
Energy-efficient and green pervasive computing
Environmental Engineering,
Estimation and identification techniques
Evaluation techniques for middleware solutions
Event-based, publish/subscribe, and message-oriented middleware
Evolutionary computing and intelligent systems
Expert approaches
Facilities planning and management
Flexible manufacturing systems
Formal methods and tools for designing
Fuzzy algorithms
Fuzzy logics
GPS and location-based app
USER EXPERIENCE AND DIGITALLY TRANSFORMED/CONVERTED EMOTIONSIJMIT JOURNAL
In human natural interaction (human-human interaction), humans use speech beside the non-verbal cues
like facial expressions movements and gesture movements to express themselves. However, in (humancomputer
interactions), computer will use the non-verbal cues of human beings to determine the user
experience and usability of any software or application on the computer. We introduce a new model called
Measuring User Experience using Digitally Transformed/Converted Emotions (MUDE) which measures
two metrics of user experience(satisfaction and errors) , and compares them with SUS questionnaire results
by conducting an experiment for measuring the usability and user’s experience.
Human-robot interaction can increase the challenges of artificial intelligence. Many domains of AI and its effect is laid down, which is mainly called for their integration, modelling of human cognition and human, collecting and representing knowledge, use of this knowledge in human level, maintaining decision making processes and providing these decisions towards physical action eligible to and in coordination with humans. A huge number of AI technologies are abstracted from task planning to theory of mind building, from visual processing to symbolic reasoning and from reactive control to action recognition and learning. Specific human-robot interaction is focused on this case. Multi-model and situated communication can support human-robot collaborative task achievement. Present study deals with the process of using artificial intelligence (AI) for human-robot interaction. by Vishal Dineshkumar Soni 2018. Artificial Cognition for Human-robot Interaction. International Journal on Integrated Education. 1, 1 (Dec. 2018), 49-53. DOI:https://doi.org/10.31149/ijie.v1i1.482. https://journals.researchparks.org/index.php/IJIE/article/view/482/459 https://journals.researchparks.org/index.php/IJIE/article/view/482
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Agent-Based Modeling for Sociologists is a crash course on how to build ABM in the social sciences. This presentation has an introduction to OOP and then discusses three models in details, along with their NetLogo implementation
CONSIDERATION OF HUMAN COMPUTER INTERACTION IN ROBOTIC FIELD ijcsit
Technological progress leads the apparition of robot in human environment; we began to find them in hospitals, museums, and homes. However these situations require an interaction of robots with humans and an adoption of social behaviors. We have shown in this paper how disciplines like computer science in general and human computer interaction in particular are used to improve human robot interaction. Then we indicated how we can use action theory into design of interaction between human and Robot. Finally we proposed some practical scenarios for illustrations.
The objective of emotion recognition is
identifying emotions of a human. The emotion can be
captured either from face or from verbal communication. In
this work we focus on identifying human emotion from
facial expressions. Facial emotion recognition is one of the
useful task and can be used as a base for many real-time
applications.
Hoe overheden en bedrijven je bespieden om je gedrag te manipuleren | Privacy...Matthijs Pontier
Hoe overheden en bedrijven je bespieden om je gedrag te manipuleren | Privacy symposium Usocia | Universiteit Utrecht | 2021-05-06
2. Vrij delen van informatie, kunst en cultuur Evidence-based policy met een lange-termijn visie Zelfbeschikking stimuleren zonder dat dit ten koste gaat van rechten Vertrouwen burgers vs Wantrouwen ‘macht' Enthousiast over tech, maar alert op risico’s Tech to empower people; niet om te onderdrukken Basis principes PPNL Matthijs Pontier, Ph.D., Privacy symposium Usocia, Universiteit Utrecht 06-05-2021
3. Matthijs Pontier, Ph.D., Privacy symposium Usocia, Universiteit Utrecht 06-05-2021
4. Amsterdam wil data verzamelen en delen, om: – Maatschappelijke vraagstukken oplossen – Dienstverlening verbeteren – Slimmer handhaven – Openbare ruimte beheren – Werkprocessen efficiënter – Zorg verbeteren – Patronen herkennen om gedrag te beïnvloeden “Amsterdam doet daarbij graag zaken met de markt”
5. Verkapte privatisering publieke diensten? Wie beheert AI in ‘slimme’ stad? Wie beheert de data die we samen produceren? Wie profiteert van de voordelen? Wie kampt met de nadelen?
6. Google ontwierp privacy policies voor buurten: - Mensen zouden getarget worden met producten - Zelfs getarget worden om stemgedrag te beïnvloeden Privacy adviseur Ann Cavoukian nam ontslag: "I imagined us creating a smart city of privacy, as opposed to a smart city of surveillance" Toronto: "The evaluation process will determine which parts of the proposal, if any, may be pursued further." Google Sidewalks Toronto Matthijs Pontier, Ph.D., Privacy symposium Usocia, Universiteit Utrecht 06-05-2021
7. Privacy in ‘smart’ cities https://piratenpar tij.nl/zorgen-om- de-privacy-in-sli mme-steden/
8. Niets te verbergen? Functie in sociale relaties Mogen anderen ook niks te verbergen hebben? Snowden: “I don't care about privacy, 'because I have nothing to hide', is like I don't care about free speech, Because I have nothing to say" Matthijs Pontier, Ph.D., Privacy symposium Usocia, Universiteit Utrecht 06-05-2021
9. Matthijs Pontier, Ph.D., Privacy symposium Usocia, Universiteit Utrecht 06-05-2021
10. 15-12-16 SyRI https://www.youtube.com/watch?v=2GkXCzYdrBY • Allerlei data die je met overheid deelt wordt aan elkaar geknoopt. • Iedereen is bij voorbaat verdacht → bestempeld met risicoprofiel. • Op basis hiervan wordt je extra in de gaten gehouden, zonder dat je dit weet. • https://bijvoorbaatverdacht.nl/ Matthijs Pontier, Ph.D., Privacy symposium Usocia, Universiteit Utrecht 06-05-2021
11. 15-12-16 SyRI in Rotterdam 25.000 bewoners Bloemhof en Hillesluis op fraude onderzocht • 50% Foutmarge • 10% werd onderzocht • 0(!!) fraudegevallen opgespoord Na ophef gestopt met experiment Matthijs Pontier, Ph.D., Privacy symposium Usocia, Universiteit Utrecht 06-05-2021
12. https://nos.nl/artikel/2366864-fraud e-opsporen-of-gevaar-van-discrimin atie-gemeenten-gebruiken-slimme- algoritmes Toeslagenschandaal Toeslagenaffaire
Piratenpartij presentatie programma en kandidaten Tweede kamerverkiezingen 2021Matthijs Pontier
PirtatenpartijTK2021
1. Visie met lef
2. ● In meer dan 60 landen actief ● Honderden zetels op verschillende niveaus ● Belangrijkste oppositiepartij in Tsjechië, IJsland ● Eerste pan-Europese partij ● Verviervoudigd in EU sinds 2014 Internationale beweging Matthijs Pontier, Ph.D., Presentatie Piratenpartij Tweede Kamerverkiezingen 2021. #StemPiraat #TK2021
3. ● Vertrouwen op mensen vs wantrouwen macht ● Enthousiast over tech, maar alert op risico’s ● Tech om te empoweren; niet om te onderdrukken ● Vrij delen van informatie, kennis en cultuur ● Evidence-based beleid met lef en lange-termijn visie Matthijs Pontier, Ph.D., Presentatie Piratenpartij Tweede Kamerverkiezingen 2021. #StemPiraat #TK2021 Basisprincipes
4. Empowerment / Decentralisatie van macht • Burgerrechten • Democratie • Transparantie • Vrijheid van informatie Kernpunten Matthijs Pontier, Ph.D., Presentatie Piratenpartij Tweede Kamerverkiezingen 2021. #StemPiraat #TK2021
5. Burgerrechten zijn grondvesten van onze beweging. Privacy, Zelfbeschikking, Vrije meningsuiting, Internettoegang, Gelijkwaardige behandeling • Stop microtargeting • Delete Big Brother: Sleepnet uit sleepwet • Transparantie en audits voor algoritmes • Tanden voor de toezichthouder: Versterk AP Burgerrechten Matthijs Pontier, Ph.D., Presentatie Piratenpartij Tweede Kamerverkiezingen 2021. #StemPiraat #TK2021
6. Open, transparante overheid is basisvoorwaarde voor een goede democratie • Stop de Rutte-doctrine! Streaming besprekingen • Transparantie kasboek, lobby, belastingafspraken • Ook bij semi-overheid en adviesorganen • Vrije, onafhankelijke media • Bescherming klokkenluiders Transparantie Matthijs Pontier, Ph.D., Presentatie Piratenpartij Tweede Kamerverkiezingen 2021. #StemPiraat #TK2021
7. Vrijheid van informatie Vrije toegang tot info, kennis en cultuur is een voorwaarde voor sociale, technologische en economische ontwikkeling. Auteursrecht en patentrecht creëren informatie monopolies die alleen grote bedrijven ten goede komen. Stel het in dienst van auteurs! Innovatie bevorderen: als je iets kopieert, verdubbel je waarde! Zo medicijnen en behandelmethoden direct wereldwijd verspreid Als doel monopolie geld verdienen is, beloon dan met geld Investeer in kennis, in plaats van het hek om de kennis Matthijs Pontier, Ph.D., Presentatie Piratenpartij Tweede Kamerverkiezingen 2021. #StemPiraat #TK2021
8. Democratie Niet eens in de vier jaar, maar het hele jaar door meebeslissen • bindend (p)referendum en volksinitiatief, zonder opkomstdrempels • e-democracy (zoals ons initiatief in Amsterdam) • burgerforum / burgerberaad / burgertop • eerlijke behandeling voor lokale politieke partijen • wetten toetsen aan de Grondwet • apolitieke benoemingen (semi-)overheid: stop de vriendjespolitiek Meer dan stemmen alleen. Democratie gaat over verhoudingen in samenleving Recht doen aan wat de meerderheid wil en rekening houden met de minderheid Daarom is combinatie met burgerrechten en stevige rechtsstaat zo belangrijk
How AI will shift the distribution of power and wealth
and what we can do about it
1. How will AI shift the distribution of power? Matthijs Pontier, Ph.D.
2. Matthijs Pontier, Ph.D., VU University Amsterdam,09-01-2020,How AI will shift the distribution of power
3. Matthijs Pontier, Ph.D., VU University Amsterdam,09-01-2020,How will AI shift the distribution of power?
4. Matthijs Pontier, Ph.D., VU University Amsterdam,09-01-2020,How will AI shift the distribution of power?
5. Is 'smart city' privatizing public services? Who controls AI in the ‘smart’ city? Who controls the data that we produce together? Who profits from the benefits? Who has to deal with the cons? Matthijs Pontier, Ph.D., VU University Amsterdam,09-01-2020,How will AI shift the distribution of power?
6. Amsterdam wants to collect and share data, to: – Solve societal problems – Improve services – Smarter law enforcement – Manage public space – Improve working processes – Improve healthcare – Recognize patterns to influence behavior “Hereby, Amsterdam is happy to do business with the market”Matthijs Pontier, Ph.D., VU University Amsterdam,09-01-2020,How will AI shift the distribution of power?
7. Google Sidewalks Toronto Matthijs Pontier, Ph.D., VU University Amsterdam,09-01-2020,How will AI shift the distribution of power?
8. Google Sidewalks Toronto Should a commercial company do this? Google? Lack of transparency People sued municipality: "We don't want to be Google lab rats" Blackberry CEO: "This is a colonizing experiment in surveillance capitalism attempting to bulldoze important urban, civic and political issues." Matthijs Pontier, Ph.D., VU University Amsterdam,09-01-2020,How will AI shift the distribution of power?
9. Google wanted a share of property taxes, development fees Monetise government transportation and mobility --> Google is trying to privatize public servicesGoogle privatizing Toronto Matthijs Pontier, Ph.D., VU University Amsterdam,09-01-2020,How will AI shift the distribution of power?
10. Google designed privacy policies city neighbourhoods.- Individuals targeted with products. - Even targeted to influence their votesPrivacy adviser Ann Cavoukian resigned:"I imagined us creating a smart city of privacy, as opposed to a smart city of surveillance" Toronto: "The evaluation process will determine which parts of the proposal, if any, may be pursued further." Google Sidewalks Toronto
11. Cars that free your time to do other things
12. Corporate surveillance: ‘Smart TV’
13. Monopolization Matthijs Pontier, Ph.D., VU University Amsterdam,09-01-2020,How will AI shift the distribution of power?
14. The bigger the company The bigger the power center The bigger the chance the company will misuse this power for its own benefit
Wie wordt de baas over onze big data? | 2019 10-29 HvAMatthijs Pontier
Wie wordt de baas over onze big data? | 2019 10-29 HvA
Vrij delen van informatie, kunst en cultuur Evidence-based policy met een lange-termijn visie Zelfbeschikking stimuleren zonder dat dit ten koste gaat van rechten Vertrouwen burgers vs Wantrouwen ‘macht' Enthousiast over tech, maar alert op risico’s Tech to empower people; niet om te onderdrukken Basis principes PPNL Matthijs Pontier, Ph.D., Hogeschoolvan Amsterdam, 29-10-2019, Wiewordtdebaas over onzebig data?
3. MatthijsPontier, Ph.D., Hogeschool van Amsterdam, 29-10-2019, Wie wordt de baasover onze big data?
4. MatthijsPontier, Ph.D., Hogeschool van Amsterdam, 29-10-2019, Wie wordt de baasover onze big data?
5. Amsterdam wil data verzamelen en delen, om: – Maatschappelijke vraagstukken oplossen – Dienstverlening verbeteren – Slimmer handhaven – Openbare ruimte beheren – Werkprocessen efficiënter – Zorg verbeteren – Patronen herkennen om gedrag te beïnvloeden “Amsterdam doet daarbij graag zaken met de markt”
6. Verkapte privatisering publieke diensten? Wie beheert AI in ‘slimme’ stad? Wie beheert de data die we samen produceren? Wie profiteert van de voordelen? Wie kampt met de nadelen?
7. Niets te verbergen? Functie in sociale relaties Mogen anderen ook niks te verbergen hebben? Snowden: “I don't care about privacy, 'because I have nothing to hide', is like I don't care about free speech, Because I have nothing to say" Matthijs Pontier, Ph.D., Hogeschoolvan Amsterdam, 29-10-2019, Wiewordtdebaas over onzebig data?
8. Mass surveillance: voor 1 target, tienduizenden bespieden Info delen met dubieuze regimes Devices bewust lek houden om ons te hacken MatthijsPontier, Ph.D., Hogeschool van Amsterdam, 29-10-2019, Wie wordt de baasover onze big data?
9. Matthijs Pontier, Ph.D., Hogeschoolvan Amsterdam, 29-10-2019, Wiewordtdebaas over onzebig data?
10. 15-12-16 SyRI https://www.youtube.com/watch?v=2GkXCzYdrBY • Allerlei data die je met overheid deelt wordt aan elkaar geknoopt. • Iedereen is bij voorbaat verdacht -> bestempeld met risicoprofiel. • Op basis hiervan wordt je extra in de gaten gehouden, zonder dat je dit weet. • https://bijvoorbaatverdacht.nl/ Matthijs Pontier, Ph.D., Hogeschoolvan Amsterdam, 29-10-2019, Wiewordtdebaas over onzebig data?
11. 15-12-16 SyRI in Rotterdam 25.000 bewoners Bloemhof en Hillesluis op fraude onderzocht • 50% Foutmarge • 10% werd onderzocht • 0(!!) fraudegevallen opgespoord Na ophef gestopt met experiment Matthijs Pontier, Ph.D., HvA, 29-10-2019, Wiewordtdebaas over onzebig data?
12. Ron Kowsoleea
13. Privacy inperken voor veiligheid? 1. Niet effectief 2. Leidt tot ongezonde machtsverhouding 3. Privacy juist voorwaarde voor veiligheid 4. Er bestaan prima alternatieven
14. Overheid niet altijd betrouwbaar Overheid laat gegevens slingeren WRR: Overheid onbetrouwbaar met gegevens. Informatie wordt gemanipuleerd. Ook overheid kan misdadig gedrag vertonen
15. Function Creep
Who controls our smart cities - CuriousU Summer School - Twente University - ...Matthijs Pontier
Who controls our smart cities - CuriousU Summer School - Twente University - 2019-08-16
1. Who controls our smart cities? Matthijs Pontier, Ph.D.
2. ⚫ Free sharing of information, art and culture ⚫ Evidence-based policy with long-term vision ⚫ Stimulate self-determination, but keep rights ⚫ Trust civilians vs Distrust ‘power' ⚫ Enthusiastic about tech, but alert on risks ⚫ Tech to empower people; not to repress Basic principles PPNL Matthijs Pontier, Ph.D., CuriousU Summer School,Twente University, 16-08-2019,Who controls our smart cities?
3. Matthijs Pontier, Ph.D., CuriousU Summer School,Twente University, 16-08-2019,Who controls our smart cities?
4. Matthijs Pontier, Ph.D., CuriousU Summer School,Twente University, 16-08-2019,Who controls our smart cities?
5. Matthijs Pontier, Ph.D., CuriousU Summer School,Twente University, 16-08-2019,Who controls our smart cities?
6. Is 'smart city' privatizing public services? Who controls AI in the ‘smart’ city? Who controls the data that we produce together? Who profits from the benefits? Who has to deal with the cons? Matthijs Pontier, Ph.D., CuriousU Summer School,Twente University, 16-08-2019,Who controls our smart cities?
7. Amsterdam wants to collect and share data, to: – Solve societal problems – Improve services – Smarter law enforcement – Manage public space – Improve working processes – Improve healthcare – Recognize patterns to influence behavior “Hereby, Amsterdam is happy to do business with the market”Matthijs Pontier, Ph.D., CuriousU Summer School,Twente University, 16-08-2019,Who controls our smart cities?
8. Cars that free your time to do other things
9. Local vs Centralized data Matthijs Pontier, Ph.D., CuriousU Summer School,Twente University, 16-08-2019,Who controls our smart cities?
10. Who will control ‘smart’ cars? Matthijs Pontier, Ph.D., CuriousU Summer School,Twente University, 16-08-2019,Who controls our smart cities?
11. Monopolization Matthijs Pontier, Ph.D., CuriousU Summer School,Twente University, 16-08-2019,Who controls our smart cities?
12. The bigger the company The bigger the power center The bigger the chance the company will misuse this power for its own benefit.. Matthijs Pontier, Ph.D., CuriousU Summer School,Twente University, 16-08-2019,Who controls our smart cities?
13. Corporate surveillance: ‘Smart TV’ Matthijs Pontier, Ph.D., CuriousU Summer School,Twente University, 16-08-2019,Who controls our smart cities?
14. If technology thinks for you, Then do the programmers tell you what to think?
15. Smart TV hackers are filming couples having sex on their sofa’s and they are putting it on porn sites! Matthijs Pontier, Ph.D., CuriousU Summer School,Twente University, 16-08-2019,Who controls our smart cities?
16. Matthijs Pontier, Ph.D., CuriousU Summer School,Twente University, 16-08-2019,Who controls our smart cities?
17. Who actually owns your 'smart home'? Hackers? Viruses? Terrorists? Companies that actually own your devices AI
How we can use technology to increase our freedom - Psychedelic Society of th...Matthijs Pontier
How we can use technology to increase our freedom - Psychedelic Society of the Netherlands
1. How we can use tech to increase our Freedom and create a Better Future and outsmart governments and companies who want the opposite Matthijs Pontier, Ph.D Candidate #2 Piratenpartij Amsterdam Vice-president ENCOD
2. Contents: • Mass-surveillance (sleepwet) • Privacy protection / Safe communication • Platform capitalism and the harms of monopolization of tech • Cooperative alternatives • Darkweb marketplaces • E-Democracy • Universal Basic Income Contents Matthijs Pontier Ph.D, Psychedelic Society of the Netherlands, 25-02-2018, How we can use tech to increase our freedom
3. Free sharing of information, art and culture Evidence-based policy with long-term vision Stimulate self-determination, but keep rights Trust civilians vs Distrust ‘power' Enthusiastic about tech, but alert on risks Tech to empower people; not to repress Basic principles PPNL Matthijs Pontier Ph.D, Psychedelic Society of the Netherlands, 25-02-2018, How we can use tech to increase our freedom
4. Why do they call the WIV dragnet surveillance?
5. How do you find a needle in a haystack?
6. By making the haystack bigger? More data is leading to less safety
7. Matthijs Pontier Ph.D, Psychedelic Society of the Netherlands, 25-02-2018, How we can use tech to increase our freedom
8. Matthijs Pontier Ph.D, Psychedelic Society of the Netherlands, 25-02-2018, How we can use tech to increase our freedom
9. Matthijs Pontier Ph.D, Psychedelic Society of the Netherlands, 25-02-2018, How we can use tech to increase our freedom
10. Matthijs Pontier Ph.D, Psychedelic Society of the Netherlands, 25-02-2018, How we can use tech to increase our freedom
11. Privacy inperken voor veiligheid? 1. Niet effectief 2. Leidt tot ongezonde machtsverhouding 3. Privacy juist voorwaarde voor veiligheid 4. Er bestaan prima alternatieven
12. The government won’t protect your privacy Matthijs Pontier Ph.D, Psychedelic Society of the Netherlands, 25-02-2018, How we can use tech to increase our freedom
13. Secret services are keeping our devices unsafe because they want to hack us Dr. Matthijs Pontier, Laat je niet meeslepen Hacking agencies: Keeping us all unsafe
14. What’s wrong with the WIV? Matthijs Pontier Ph.D, Psychedelic Society of the Netherlands, 25-02-2018, How we can use tech to increase our freedom
15. What’s wrong with the WIV?
17. Tech companies (like cloud services) will move out for safe harbours Matthijs Pontier Ph.D, Psychedelic Society of the Netherlands, 25-02-2018, How we can use tech to increase our freedom Tech economy
18. Enrypt your e-mail Protonmail Encrypt phone Signal adblockers DNT uBlock / DNT TOR VPN
Informatieve Piratenpartij-presentatie over de Wet op de Inlichtingen en Veiligheidsdiensten (WIV), oftewel de Sleepwet
1. Laat jij je meeslepen? Dr. Matthijs Pontier, Kandidaat #2 Piratenpartij Amsterdam
2. Waarom noemen ze ‘WIV’ eigenlijk ‘Sleepwet’?
3. Waar ik het over ga hebben: • Wat er vooraf ging • Wat is er goed aan de WIV .. wat is er mis mee • Wat kunnen we doen • Referendum • Rechtszaak
7. De WIV WIV
10. Eindverantwoordelijkheid op voorgenomen toezicht blijft bij de minister liggen Wat is er slecht?
11. hoe vind je een speld in een hooiberg?
12. Met een grotere hooiberg? Meer data is niet meer Veiligheid
13. •Iedereen heeft wel wat te verbergen •Functie in sociale relaties •Als je niks te verbergen hebt, mogen anderen dan ook niks te verbergen hebben? •Snowden: “Ik vind privacy niet belangrijk, want ik heb toch niets te verbergen Is als Ik vind vrijheid meningsuiting niet belangrijk, want ik heb toch niets te melden”
14. Privacy inperken voor veiligheid? 1. Niet effectief 2. Leidt tot ongezonde machtsverhouding 3. Privacy juist voorwaarde voor veiligheid 4. Er bestaan prima alternatieven
15. Dr. Matthijs Pontier, Laat jij je meeslepen?
16. Overheid niet altijd betrouwbaar • Overheid laat gegevens slingeren • WRR: Overheid onbetrouwbaar met gegevens. Informatie wordt gemanipuleerd. • Ook overheid kan misdadig gedrag vertonen
17. ‘Broedkamer’ Belastingdienst • SyRI, Systeem Risico Indicatie (Wet SUWI) tbv fraudepreventie Hierover zei de Raad van State: ‘Er [is] nauwelijks een persoonsgegeven te bedenken dat niet voor verwerking in aanmerking komt. De opsomming lijkt niet bedoeld om in te perken, maar om zoveel mogelijk armslag te hebben.’
18. DNA databank • AIVD/MIVD gaan zelf DNA materiaal verzamelen • Uit database ‘DNA voor veroordeelden’ maar ook via hacking • Database is geheim; je kunt niet controleren of jij of je familie erin staat
19. • Techbedrijven (zoals clouddiensten) zullen naar veilige plek vluchten Vestigingsklimaat tech-bedrijven
20. Onze devices worden bewust onveilig gehouden om ons te kun
ENCOD jaarplan voor 2018
1. ENCOD’s plannen voor 2018 Matthijs Pontier, Ph.D., Vice-president ENCOD
2. Doelen ENCOD Eerlijk en efficiënt drugsbeleid Rechten van producenten en consumenten beschermen Informatie verstrekken over veilige teelt / veilig gebruik Matthijs Pontier, Ph.D., ENCOD’s plannen voor 2018, 14-12-2017
3. ENCOD nu ~150 leden, aantal neemt gestaag toe 7 mensen in Executive Committee (vrijwel) volledig vrijwillig: Mauro Picavet President Nederland Matthijs Pontier Vice-president Nederland Gabrielle Kozar Penningmeester Oostenrijk Nico Vlaming Secretaris Nederland Ana Afuera Spanje Maja Kohek Slovenie Enrico Fletzer Italie Matthijs Pontier, Ph.D., ENCOD’s plannen voor 2018, 14-12-2017
4. Huidige activiteiten ENCOD Organisatie heropgebouwd en hervormd Beurzen en evenementen bezocht Bestaande contacten opnieuw opgepakt Contact met politici voor verbetering beleid ECOSOC status aangevraagd voor politieke campagnes Website vernieuwen Informatie verspreid over ontwikkelingen drugsbeleid via Nieuwsbrief, Bulletin, Facebook, Twitter Netwerkbijeenkomst Matthijs Pontier, Ph.D., ENCOD’s plannen voor 2018, 14-12-2017Matthijs Pontier, Ph.D., ENCOD’s plannen voor 2018, 14-12-2017
5. Holarchie Horizontale organisatie, maar met structuur Doelen, rollen, taken definiëren Met een rol / taak, komt Verantwoordelijkheid, Vrijheid en Vertrouwen Meritocratie Structuur organisatie is volledig transparant Dit maakt directe communicatie mogelijk Matthijs Pontier, Ph.D., ENCOD’s plannen voor 2018, 14-12-2017
6. Holarchie Matthijs Pontier, Ph.D., ENCOD’s plannen voor 2018, 14-12-2017
7. ENCOD ENCOD NL ENCOD NL Penningmeester EC Organizer Secretaris Vertaler Webmaster Ontwerper Comunicator CSC-contact Matthijs Pontier, Ph.D., ENCOD’s plannen voor 2018, 14-12-2017
8. Holarchy Matthijs Pontier, Ph.D., ENCOD’s plannen voor 2018, 14-12-2017
9. ENCOD in 2018 Campagnes: Cannnabis Social Clubs: Good practices guide en consultancy Freedom to farm Seeds for Drug Peace Global Marijuana March Politiek: Lobby EU / VN / lidstaten Database met situatie politiek en drugsbeleid per land Infopakket voor lokale overheden / lokale politiek Info: Archief van eerdere presentaties / lezingen Advocaten database Matthijs Pontier, Ph.D., ENCOD’s plannen voor 2018, 14-12-2017
10. Cannabis Social Clubs: Good Practices Guide Good Practices Guide ontwikkelen Consultancy om te helpen met opstarten / hulp bij problemen CATEGORY AMOUNT IN € Web page creation 2.000 Print materials (banners, flyers, stickers, etc.) 1.000 / year Online advertising 100 / month Coordination + webadmin + communication worker fees (0,2 fte total) 400 / month Consultancy fees (0,4 fte) 800 / month TOTAL FIRST YEAR 18.600 TOTAL NEXT YEARS 16.600Matthijs Pontier, Ph.D., ENCOD’s plannen voor 2018, 14-12-2017
11. Freedom to farm Campage voor zelfbeschikkingsrecht Vrije en Veilige Cultivatie
Wie krijgt de controle over onze data - Inholland Diemen - 2017 11-23 Matthijs Pontier
1. Wij krijgt controle over onze Big Data? Matthijs Pontier, Ph.D.
2. Vrij delen van informatie, kunst en cultuur Evidence-based policy met een lange-termijn visie Zelfbeschikking stimuleren zonder dat dit ten koste gaat van rechten Vertrouwen in goedheid mensen vs Wantrouwen machtsstructuren Enthousiast over mogelijkhede tech, maar alert op risico’s Tech to empower people; niet om te onderdrukken Basis principes PPNL Matthijs Pontier, Ph.D, 24-11-2017, Inholland Diemen, Wie krijgt controle over onze Big Data?
3. Monopolization Matthijs Pontier, Ph.D, 24-11-2017, Inholland Diemen, Wie krijgt controle over onze Big Data?
4. Hoe groter het bedrijf Hoe groter het machtscentrum Hoe groter de kans dat een bedrijf deze macht op een verkeerde manier gaat gebruiken Matthijs Pontier, Ph.D, 24-11-2017, Inholland Diemen, Wie krijgt controle over onze Big Data?
5. Corporate surveillance: ‘Smart TV’ Matthijs Pontier, Ph.D, 24-11-2017, Inholland Diemen, Wie krijgt controle over onze Big Data?
6. Smart TV hackers filmen mensen die seks hebben op hun bank en plaatsen het op porno sites! Matthijs Pontier, Ph.D, 24-11-2017, Inholland Diemen, Wie krijgt controle over onze Big Data?
7. Matthijs Pontier, Ph.D, 24-11-2017, Inholland Diemen, Wie krijgt controle over onze Big Data?
8. Wie wordt de baas in jouw ‘slimme’ huis? Hackers? Virussen? Terroristen? Bedrijven die eigenlijk je apparaat bezitten in praktijk? De AI die vertelt wat je moet doen? Of krijgen ze een eigen wil? Matthijs Pontier, Ph.D, 24-11-2017, Inholland Diemen, Wie krijgt controle over onze Big Data?
9. Verkapte privatisering publieke diensten? Wie beheert AI in ‘slimme’ stad? Wie beheert de data die we samen produceren? Wie profiteert van de voordelen? Wie kampt met de nadelen? Matthijs Pontier, Ph.D, 24-11-2017, Inholland Diemen, Wie krijgt controle over onze Big Data?
10. Matthijs Pontier, Ph.D, 24-11-2017, Inholland Diemen, Wie krijgt controle over onze Big Data?
11. Responsible Digital Cities Bewoners bepalen wat er met hun data gebeurt: Privacy = Ownership Bewoners bepalen hoe digitale stad zich ontwikkelt Democratie Verzamelde data is gemeenschapsgoed: Commons Openheid over dataverzameling en verwerking Transparantie Iedereen kan meedoen: Inclusie Menselijke maat staat centraal in alles wat we doen Empathie Matthijs Pontier, Ph.D, 24-11-2017, Inholland Diemen, Wie krijgt controle over onze Big Data?
12. Matthijs Pontier, Ph.D, 24-11-2017, Inholland Diemen, Wie krijgt controle over onze Big Data?
13. Privacy inperken voor veiligheid? 1. Niet effectief 2. Leidt tot ongezonde machtsverhouding 3. Privacy juist voorwaarde voor veiligheid 4. Er bestaan prima alternatieven
14. Niets te verbergen? Iedereen heeft wel wat te verbergen Functie in sociale relaties Als je niks te verbergen hebt, mogen anderen dan ook niks te verbergen hebben? Snowden: “Ik vind privacy niet belangrijk, want ik heb toch niets te verbergen” “Ik vind vrijheid meningsuiting
How we can use technology to increase our freedom - and outsmart governments ...Matthijs Pontier
Psyi 2017 how use tech to improve freedom
How we can use technology tu increase our freedom - and outsmart governments and companies who want the opposite - Lecture at Psy-Fi 2017
1. How we can use technology to increase our freedom and outsmart governments and companies who want the opposite Dr. Matthijs Pontier
2. Contents: • Lessons from tech dystopia’s • Self-driving cars as an example • Platform capitalism and the harms of monopolization of tech • Privacy protection / Safe communication • Machine ethics • Darkweb marketplaces • Universal Basic Income • E-Democracy Contents Matthijs Pontier, Psy-Fi 2017, 16-08-2017, How we can use tech to increase our freedom
3. Free sharing of information, art and culture Evidence-based policy with long-term vision Stimulate self-determination, but keep rights Trust civilians vs Distrust ‘power' Enthusiastic about tech, but alert on risks Tech to empower people; not to repress Basic principles PPNL Matthijs Pontier, Psy-Fi 2017, 16-08-2017, How we can use tech to increase our freedom
4. Using tech for good: Improving autonomy Matthijs Pontier, Psy-Fi 2017, 16-08-2017, How we can use tech to increase our freedom
5. Drinking
6. Road safety ;) Matthijs Pontier, Psy-Fi 2017, 16-08-2017, How we can use tech to increase our freedom
7. Matthijs Pontier, Psy-Fi 2017, 16-08-2017, How we can use tech to increase our freedomMatthijs Pontier, Psy-Fi 2017, 16-08-2017, How we can use tech to increase our freedom
8. More room for people and plants
9. Matthijs Pontier, Psy-Fi 2017, 16-08-2017, How we can use tech to increase our freedom
10. Matthijs Pontier, Psy-Fi 2017, 16-08-2017, How we can use tech to increase our freedom
11. Who will control ‘smart’ cars? Matthijs Pontier, Psy-Fi 2017, 16-08-2017, How we can use tech to increase our freedom
12. Who will control ‘smart’ cars? Hackers? Viruses? Terrorists? Car company that calls them back if you don’t pay? The AI that tells you what to do? Or will they control themselves? Matthijs Pontier, Psy-Fi 2017, 16-08-2017, How we can use tech to increase our freedom
13. Local vs Centralized data Matthijs Pontier, Psy-Fi 2017, 16-08-2017, How we can use tech to increase our freedom
14. Monopolization Matthijs Pontier, Psy-Fi 2017, 16-08-2017, How we can use tech to increase our freedom
15. The bigger the company -> Bigger power concentration -> Bigger chance that this company will use its power in malicious ways Matthijs Pontier, Psy-Fi 2017, 16-08-2017, How we can use tech to increase our freedom
16. Need to stop corporate surveillance Regulate Big Business vs More freedom for small startups
17. Safe communication is necessary for progress We won’t know what will be prohibited when and where Self-censorship If we would have had current control systems in the past, would homosexuality still be illegal? Matthijs Pontier, Psy-Fi 2017, 16-08-2017, Using tech to increase freedom
18. The government won’t protect your privacy
Wie de data controleert, heeft de macht - Symposium sticky / gastles UniC, U...Matthijs Pontier
1984, amsterdam, amsterdam-west, anpr, cameratoezicht, democratie, function creep, gemeenteraad, huxley, marcouch, orwell, panopticon, piratenpartij, politiek, ppnl, preventief fouilleren, privacy, surveillance, veiligheid, waterbedeffect
1. Het belang van privacy als je niets te verbergen hebt Dr. Matthijs Pontier
2. Privacy inperken voor veiligheid? 1. Niet effectief 2. Leidt tot ongezonde machtsverhouding 3. Privacy juist voorwaarde voor veiligheid 4. Er bestaan prima alternatieven
3. Niets te verbergen? • Iedereen heeft wel wat te verbergen • Functie in sociale relaties • Als je niks te verbergen hebt, mogen anderen dan ook niks te verbergen hebben? • Snowden: “Ik vind privacy niet belangrijk, want ik heb toch niets te verbergen”, is als “Ik vind vrijheid meningsuiting niet belangrijk, want ik heb toch niets te melden”
4. Ron Kowsoleea
5. Scheve Machtsverhouding Burger - Overheid – Reisgedrag en function creep – Bespieden telefoon- en computergebruik – Internet of Things Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
6. Vrije samenleving nodig voor vooruitgang • We weten niet wat waar wanneer fout is Zelfcensuur • Met huidige controlesystemen was homoseksualiteit nog steeds illegaal geweest Dr. Matthijs Pontier, UUMUN TEDx
7. Overheid niet altijd betrouwbaar • Overheid laat gegevens slingeren • WRR: Overheid onbetrouwbaar met gegevens. Informatie wordt gemanipuleerd. • Ook overheid kan misdadig gedrag vertonen
8. Sleepnetmethode + Profiling • Discriminatie zonder te weten waarop • Speuren naar ‘afwijkend gedrag’ Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
9. Slachtoffers van Profiling
13. Slachtoffers van Profiling: Daniel
14. Corporate surveillance: ‘Smart TV’ Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
15. Smart TV hackers are filming couples having sex on their sofa’s and they are putting it on porn sites! Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
16. Smart technology • Als technologie voor je denkt bepalen programmeurs dan wat je denkt? Dr. Matthijs Pontier, Piratenpartij – HvA, HBO Rechten, 2-9-2016
17. Wanneer algoritmes ons controleren... Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
19. <slide> Dr. Matthijs Pontier, UUMUN TEDx, Utrecht, 30-9-2016
20. • Enrypt e-mail Protonmail • Encrypt phone / messaging Signal • Gebruik adblockers en DNT uBlock / DNT • Iets van het Internet afhalen bestaat niet Privacy: Oplossingen
21. Veilig en vrij • Privacy juist voorwaarde voor veiligheid • Herstel machtsverhouding burger overheid • Kies voor effectieve maatregelen – Gerichte surveillance
22. Bedankt! • @Matthijs85 • Matthijs Pontier • matthijs@piratenpartij.nl • http://www.piratenpartij.nl/ • @Piratenpartij •
Hoe de Piratenpartij de rechtsstaat wil beschermen en waarom je deze presenta...Matthijs Pontier
Hoe de Piratenpartij de rechtsstaat wil beschermen en waarom je deze presentatie met iedereen mag delen - Presentatie voor Digi Juridica, VU University Amsterdam
Hoe we kunnen zorgen dat iedereen profiteert van robotisering | ConferentieSo...Matthijs Pontier
Hoe we kunnen zorgen dat iedereen profiteert van robotisering Dr. Matthijs Pontier
2. Waarom ontwikkelen we technologie? Evolutionaire vooruitgang Het laten voortbestaan van (menselijk) leven Om ons welzijn te vergroten Dr. Matthijs Pontier, Sociale Innovatie, 16-12-2016, Hoe kunnen we zorgen dat iedereen van robotisering profiteert
3. Dr. Matthijs Pontier, Sociale Innovatie, 16-12-2016, Hoe kunnen we zorgen dat iedereen van robotisering profiteert
4. Vrij delen van informatie, kunst en cultuur Evidence-based policy met een lange-termijn visie Zelfbeschikking stimuleren zonder dat dit ten koste gaat van rechten Vertrouwen burgers vs Wantrouwen ‘macht' Enthousiast over tech, maar alert op risico’s Tech to empower people; niet om te onderdrukken Basis principes PPNL Dr. Matthijs Pontier, Sociale Innovatie, 16-12-2016, Hoe kunnen we zorgen dat iedereen van robotisering profiteert
5. Welzijn en autonomie vergroten Dr. Matthijs Pontier, Sociale Innovatie, 16-12-2016, Hoe kunnen we zorgen dat iedereen van robotisering profiteert
6. Cars that free your time to do other things
7. Drinking Dr. Matthijs Pontier, Sociale Innovatie, 16-12-2016, Hoe kunnen we zorgen dat iedereen van robotisering profiteert
8. Road safety ;) Dr. Matthijs Pontier, Sociale Innovatie, 16-12-2016, Hoe kunnen we zorgen dat iedereen van robotisering profiteert
9. Road safety Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
10. Less road rage
11. More room for people and plants
12. Boosting highway capacity 273%
13. Boosting Highway capacity Dr. Matthijs Pontier, Sociale Innovatie, 16-12-2016, Hoe kunnen we zorgen dat iedereen van robotisering profiteert
14. Save energy
15. Gedeelde verantwoordelijkheid Stop met het bouwen van nieuwe wegen Stop met het bouwen van nieuwe parkeerplaatsen Dr. Matthijs Pontier, Sociale Innovatie, 16-12-2016, Hoe kunnen we zor
16. Governments are lagging behind Governments are lagging behind
17. Monopolization Dr. Matthijs Pontier, Sociale Innovatie, 16-12-2016, Hoe kunnen we zorgen dat iedereen van robotisering profiteert
18. Hoe groter het bedrijf Hoe groter het machtscentrum Hoe groter de kans dat een bedrijf deze macht op een verkeerde manier gaat gebruiken
19. Need to stop corporate surveillance Regulate Big Business vs More freedom for small startups
20. Unequal power distribution
21. Dr. Matthijs Pontier, Sociale Innovatie, 16-12-2016, Hoe kunnen we zorgen dat iedereen van robotisering profiteert
22. Techno-progressivism Democratiseer de ontwikkeling van technologie Kosten, risico’s en opbrengsten worden eerlijk gedeeld Ontwikkel technologie op zo’n manier dat het welzijn bevordert
23. Dr. Matthijs Pontier, Sociale Innovatie, 16-12-2016, Hoe kunnen we zorgen dat iedereen van robotisering profiteert
24. Dr. Matthijs Pontier, Sociale Innovatie, 16-12-2016
25. Als technologie voor je denkt Vertellen programmeurs dan
Het draait om onze ideeen - pitch lijsttrekkersverkiezingen piratenpartij TK2...Matthijs Pontier
Zoals jullie weten, sta ik hier omdat ik lijsttrekker wil worden
en eigenlijk voelt dat een beetje raar,
want ik praat veel liever over waarom onze ideen beter zijn dan die van andendere partijen,
dan dat ik moet gaan vertellen waarom ik beter zou zijn dan iemand anders,
maar juist dat maakt mij misschien wel een goede lijsttrekker.
Want het is juist een van de grootste probleem in onze samenleving
dat sommige mensen denken dat ze beter zijn dan anderen
en dan de baas gaan spelen over hen.
De geschiedenis laat zien dat macht corrumpeert.
Bij overheden, bewiendsleden, maar ook bedrijven zoals Google, Facebook en de farmaceutische industrie.
Doordat we nu met het internet met elkaar verbonden zijn,
kunnen we met de massa sterker zijn dan wie of wat dan ook.
Als je mensen wil overtuigen, moet je niet alleen weten wat je vindt,
maar vooral ook waarom je het vindt.
Bij ons ligt de kern daarbij in decentralisatie van macht.
Van daaruit kun je al onze kernpunten beredeneren.
Want we vinden privacy niet alleen maar belangrijk omdat we anders bespied worden,
maar ook omdat anders de controleurs te machtig zijn.
We vinden democratie belangrijk omdat anders de bestuurders te machtig worden.
En we vinden vije Informatie belangrijk, want kennis is macht, en die moet zich dus zoveel mogelijk verspreiden.
In de Tweede Kamer moeten we natuurlijk onze standpunten op een heleboel verschillende onderwerpen gaan bepalen, of we dat nou willen of niet.
Dan is het dus belangrijk dat we dat doen op een manier waarop we ook met elkaar verbonden blijven
Ik ben naar IJsland gegaan om te kijken hoe ze dat daar doen,
want daar gaat het eigenlijk heel goed.
En ik het daar ook uitgebreid met Brigitta over gepraat,
en ook zij zei: we focusen vooroal op democratie en decentralisatie
op die manier kunnen we samen standpunten bepalen over een heleboel onderwerpen,
zoals bijvoorbeeld ook het basisinkomen.
En dat kan dus ook,
zolang je altijd maar vanuit de kern blijft redeneren
en prioriteit blijft geven aan de belangrijkste kernpunten
Ik praat graag over deze visie.
Dat deed ik als wetenschapper,
maar dat ben ik bij de Piratenpartij natuurlijk alleen nog maar meer gaan doen,
ook buiten campagnetijd.
Ik heb stukken geschreven, interviews, mediaoptredens,
maar daar ga ik nu verder niet teveel over zeggen.
Ik heb een mooi lijstje gemaakt van 17 kantjes,
waarop jullie het allemaal kunnen terugzien en teruglezen, als jullie willen.
Ik heb veel debatten gevoerd en krijg daar vaak enthousiaste reacties over;
grote groepen mensen toegesproken op demonstraties
Ik werk graag samen met andere bewgingen en organisaties;
haal goede mensen bij de Piratenpartij, zols Jelle, Ancilla, en Niels waarmee we veel ludieke acties hebben gedaan.
Ik vind het leuk om samen met anderen projecten op te zetten,
bijvoorbeeld Crowdsource Europe
How autonomous cars are going to change our future - and what we can do to op...Matthijs Pontier
How autonomous cars are going to change our future - and what we can do to optimize that
How self-driving cars are going to change the future of mobility dr. Matthijs Pontier
2. Science Fiction becoming reality Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
3. History • Autopiloting in airplanes since 1914 • PRT-systems since 1975 • London Heathrow since 2011 Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
4. Total Recall Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
5. Now • Delivery drones • Driverless public transport • Driverless personal cars • Car-sharing increasingly popular • Driverless will become standard in 2025~2035 Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
6. Without hands Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
7. Cars that free your time to do other things
8. More comfort Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
9. Road safety ;) Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
10. Drinking Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
11. Less road rage Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
12. No more speeding tickets or parking fees Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
13. Why bother owning a car? • Building vehicles vs Enabling mobility • Making cars look different is easy • No more parking lots Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
14. More room for people and plants Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
15. Road safety Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
16. Boosting highway capacity 273% Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
17. Boosting Highway capacity Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
18. Driving fast and safe Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
19. Save energy Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
20. More traffic at night Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
21. Less flying Matthijs Pontier, 22-03-2016 TU/e, How self-driving cars are going to change the future of mobility
22. Putting a halt to urbanization?
Presentatie over grondwettelijke toetsing
Artikel 120
Art120Gw
1. Geef de grondwet (eindelijk eens) waarde dr. Matthijs Pontier, Piratenpartij
2. Inhoud • Huidige hypocrisie • Wat grondwettelijke toetsing in het buitenland oplost • Wat grondwettelijke toetsing in Nederland zou kunnen oplossen Matthijs Pontier, Artikel120Gw, Utrecht, 21-3-2016, Geef de Grondwet eindelijk eens waarde
3. Hypocrisie over Hongarije • De grondwet wordt ingeperkt, maar er wordt tenminste nog wel aan getoetst (alhoewel dit alleen op procedurele gronden mag) • In strijd met artikel 2 VEU? Hoe zit het dan met Artikel 120 Gw? Matthijs Pontier, Artikel120Gw, Utrecht, 21-3-2016, Geef de Grondwet eindelijk eens waarde
4. Hypocrisie over Hongarije • De grondwet wordt ingeperkt, maar er wordt tenminste nog wel aan getoetst (alhoewel dit alleen op procedurele gronden mag) • In strijd met artikel 2 VEU? Hoe zit het dan met Artikel 120 Gw? • Artikel 2 VEU: Volgens deze bepaling berust de Europese Unie op de waarden van vrijheid, democratie, gelijkheid en de rechtsstaat, en hebben de lidstaten deze waarden gemeen. Lidstaten kunnen dus niet volledig naar believen hun constituties inrichten, ook al is hiervoor voldoende draagvlak in een land zelf. http://www.publiekrechtenpolitiek.nl/hongarije-tart-eu/ Matthijs Pontier, Artikel120Gw, Utrecht, 21-3-2016, Geef de Grondwet eindelijk eens waarde
5. En over Polen.. • EU: “Stel Polen onder toezicht” Matthijs Pontier, Artikel120Gw, Utrecht, 21-3-2016, Geef de Grondwet eindelijk eens waarde
6. Grondwet is nu (formeel) eigenlijk WC-papier? Matthijs Pontier, Artikel120Gw, Utrecht, 21-3-2016, Geef de Grondwet eindelijk eens waarde
7. • Artikel 1 discriminatieverbod • Artikel 2 III, IV uitlevering en verlaten van het land • Artikel 3 benoeming in openbare dienst • Artikel 4, 54, 56, 129 actief en passief kiesrecht • Artikel 5 verzoekrecht • Artikel 6 vrijheid van godsdienst/levensovertuiging • Artikel 7 vrijheid van meningsuiting • Artikel 8 recht van vereniging • Artikel 9 recht van vergadering en betoging • Artikel 10 I recht op eerbiediging persoonlijke levenssfeer • Artikel 11 recht op onaantastbaarheid van lichaam • Artikel 12 binnentreden in een woning • Artikel 13 brief-, telefoon- en telegraafgeheim • Artikel 14 eigendomsrecht/onteigening • Artikel 15 vrijheidsontneming • Artikel 16 geen strafbaarheid zonder daaraan voorafgegane wettelijke strafbepaling • Artikel 17 toegang tot de rechter • Artikel 18 I rechtsbijstand • Artikel 19 III recht op vrije arbeidskeuze • Artikel 23, II-VII: vrijheid van onderwijs • Artikel 99 vrijstelling van militaire dienst wegens gewetensbezwaren • Artikel 113 III vrijheidsstraf uitsluitend door rechterlijke macht opgelegd • Artikel 114 geen doodstraf • Artikel 121 openbare en gemotiveerde rechtspraak
8. Corruptie tegenhouden (Italie) • Immuniteit voor belastingontwijking Berlusconi
Berlusconi zette buitenlandse vennootschappen op, zodat Berlusconi's bedrijven belasting ko
Exponential technology - How do we make sure everyone benefits?Matthijs Pontier
Exponential technology - How do we make sure everyone benefits?
Democracy, democratic transhumanism, technoprogressivism, Exponential Technology How can we make sure everyone benefits? Dr. Matthijs Pontier
2. Content • Why should everyone benefit? • How do we make sure everyone benefits? • Democratic Transhumanism • Machine Ethics • When we succeed New ethical dilemma’s Matthijs Pontier, Leiden, 27-2-2016 Where is the boundary of the human? The new technological turn
3. Why should everyone benefit? Who is everyone? • Does life matter? Matthijs Pontier, Leiden, 27-2-2016Where is the boundary of the human? The new technological turn
4. Why should everyone benefit? Who is everyone? • What is life, anyway? Matthijs Pontier, Leiden, 27-2-2016
5. Why should everyone benefit? Who is everyone? • Does life matter? • What is life, anyway? • Does human life matter? Matthijs Pontier, Leiden, 27-2-2016
6. Why should everyone benefit? Who is everyone? • Does life matter? • What is life, anyway? • Does human life matter? • Do human individuals matter? Matthijs Pontier, Leiden, 27-2-2016
7. Why build technology? Matthijs Pontier, Leiden, 27-2-2016 Where is the boundary of the human? The new technological turn
8. Why build technology? Matthijs Pontier, Leiden, 27-2-2016 Where is the boundary of the human? The new technological turn
9. Why build technology?
10. Why build technology? Matthijs Pontier, Leiden, 27-2-2016 Where is the boundary of the human? The new technological turn
11. Why build technology? • Evolutionary progress • Preserve (human) life • To preserve human rights • To improve our well-being Matthijs Pontier, Leiden, 27-2-2016 Where is the boundary of the human? The new technological turn
12. Why build technology? • Evolutionary progress – Description of a process or goal in itself? • Preserve (human) life • To preserve human rights • To improve our well-being Matthijs Pontier, Leiden, 27-2-2016 Where is the boundary of the human? The new technological turn
13. Why build technology? • Evolutionary progress / Preserve humans • To preserve human rights • To improve our well-being • Hedonic utilitarianism? Matthijs Pontier, Leiden, 27-2-2016
14. Why build technology? • Evolutionary progress / Preserve humans • To preserve human rights • To improve our well-being • Hedonic utilitarianism? Matthijs Pontier, 15. Brave New World
17. How do we promote happiness?
18. Promoting happiness: Kill all unhappy people? 19. Promoting happiness: Improving autonomy? 20. How do we promote autonomy? • If everyone uses cognitive enhancers, do you still have a free choice to use it yourself?
1984, ai, autonomy, brave new world, democracy, democratic transhumanism, ethics, healthcare, human enhancement, huxley, ik ben alice, machine ethics, open source, orwell, philosophy, robots, science, tech, technology, technoprogressivism
Presentatie over Piratenpartij voor 5VWO Goois Lyceum Bussum
Tegen macht Voor vrijheid dr. Matthijs Pontier Lijsttrekker Piratenpartij EP2014 Duolid Waterschap Amstel, Gooi en Vecht
2. Zeepiraten / Vrijbuiters Woordpiraten Radiopiraten Internetpiraten Geschiedenis piraterij
3. 18-02-16
4. Piratenpartij is Internationaal
5. Vertrouwen burgers vs Wantrouwen 'macht' Mensen zoveel mogelijk zelf laten doen, Maar altijd zorgen dat rechten behouden blijven Enthousiast over mogelijkheden technologie Maar alert op risico’s en mogelijk misbruik Vrij delen van kennis, kunst en cultuur Evidence-based beleid met lange-termijn visie Met oplossingen komen Basisprincipes
6. 18-02-16 Mass-surveillance Vrijheidsinperkingen Ondoorzichtig top-down bestuur Privacybescherming Zelfbeschikking E-Democracy Verschillende vormen van macht en oplossingen: Tegenmacht
7. 18-02-16
8. 18-02-16
9. 18-02-16
10. 18-02-16
11. 18-02-16
12. Privacy inperken voor veiligheid? • Leidt tot ongezonde machtsverhouding • Privacy juist voorwaarde voor veiligheid • Niet effectief
13. Evaluatie Cameratoezicht Amsterdam-West • 130.000,- excl. Personeelskosten voor 3 camera’s • Beelden waren nooit bruikbaar voor ingrijpen Cameratoezicht = Dure veiligheidsgevoelens
14. Algemeen onderzoek naar effectiviteit cameratoezicht • Bordje ophangen vaak effectiever • Beelden zelden bruikbaar voor opsporing of berechting • Slechts preventief effect calculerende daders • Waterbedeffect: Verplaatsing criminaliteit
15. 18-02-16 Veiligheid Veiligheid vs Onveiligheidsgevoel We geven jaarlijks 13 miljard uit aan veiligheid Afgelopen 11 jaar zijn gemeentes 141% meer gaan uitgeven aan veiligheidsbeleid
16. Niets te verbergen? • Iedereen heeft wel wat te verbergen • Functie in sociale relaties • Als je niks te verbergen hebt, mogen anderen dan ook niks te verbergen hebben?
17. Preventief fouilleren
18. Scheve Machtsverhouding Burger - Overheid – Reisgedrag • Criminelen kraken productie kentekenplaten – Bespieden telefoon- en computergebruik – Function creep
19. Profiling • Discriminatie zonder te weten waarop • Speuren naar ‘afwijkend gedrag’
20. Slachtoffers van Profiling
21. Slachtoffers van Profiling
22. Slachtoffers van Profiling
23. Slachtoffers van Profiling
24. Slachtoffers van Profiling
25. Slachtoffers van Profiling
26. Overheid niet altijd betrouwbaar • Overheid laat gegevens slingeren – ¼ Rechercheurs wil geen DNA afstaan • WRR: Overheid is onbetrouwbaar met gegevens. Informatie wordt gemanipuleerd • Ook overheid kan misdadig gedrag vertonen
27. Vrije samenleving nodig voor vooruitgang • Was met huidige controlesystemen homoseksualiteit nog steeds illegaal geweest? • We weten niet wat, waar, wanneer fout is Zelfcensuur "If there is no right to privacy, there can be no true freedom of expression and opinion, and
Waarom we cannabis moeten legaliseren (en andere drugs trouwens ook)Matthijs Pontier
Waarom we cannabis moeten legaliseren (en andere drugs trouwens ook)
Can’t or Cannabis met de Jonge Democraten in Groningen
1. Waarom we cannabis moeten legaliseren (en andere drugs trouwens ook) Matthijs Pontier Can’t or Cannabis, Jonge Democraten Groningen, 1-9-2015 @Matthijs85 Matthijs Pontier matthijs@piratenpartij.nl Matthijs Pontier
2. Huidige Taboesfeer Recreatief drugsgebruik vindt plaats in alle lagen van de bevolking Toch hardnekkig taboe op recreatief drugsgebruik Recreatief drugsgebruikers worden gestigmatiseerd
3. Gevolgen voor Criminaliteit ‘Belachelijke’ wetten als drugsverboden Mensen verliezen respect voor rechtssysteem Kansen gebruikers die niemand kwaad doen worden vernietigd
4. Gevolgen criminaliteit Drugsverboden Illegale, zwarte markt Ontstaan georganiseerde criminaliteit 75% georganiseerde criminaliteit gericht op productie van en handel in drugs Financiering andere criminele activiteiten Drugsgeld witwassen
5. Falen Wietpas
6. Financiële gevolgen Criminaliteit drugsverboden kost NL 17 miljard / jaar Oftewel: Ruim 1000 euro per Nederlander per jaar!! Politie en Justitie kunnen tijd nuttiger besteden: Politie is 50% tijd kwijt aan handhaving drugsverboden Rechercheurs 75% tijd kwijt aan handhaving drugsverboden Justitie is 66% tijd kwijt aan drugs-zaken Illegale drugsmarkt wereldwijd $300-800 miljard / jaar Over een illegale markt wordt geen belasting betaald
7. Handhavingsgeld in bodemloze put Repressieve maatregelen beperken drugsgebruik niet Politie: 94% georganiseerde criminaliteit houdt zich (o.a.) bezig met cannabis
8. Drugsverboden beperken Vrijheid Drugsbezit is slachtofferloze ‘misdaad’ Drugsgebruik is inperking zelfbeschikkingsrecht Drugsgebruik kan voordelen bieden: Een (nog) leukere avond Een therapeutische ervaring Om ziekte te behandelen (cannabis: MS, kanker) Onderzoek (Muller & Schumann, 2012): Meeste drugsgebruik biedt meer voordelen dan nadelen Slechts fractie drugsgebruik is problematisch
9. Discriminatie drugsgebruikers • Ongezond eten, alcohol drinken is verslavend • Extreme sporten als motorracen zijn gevaarlijk Mensen die drugs gebruiken worden gediscrimineerd ten opzichte van liefhebbers van andere risicovolle activiteiten
10. Legaal aanbod leidt niet tot hogere vraag • NL relatief vrij beleid, maar lage gebruikscijfers: • Regelmatig Cannabis: NL 6,1% GB 10,8% VS 12,6% • Met name probleemgebruik NL lager dan omringende landen • VS: jongeren krijgen makkelijker cannabis dan alcohol • Internationale analyse effecten drugsbestrijding: Totaal geen effect!! Alleen Harm Reduction werkt. • Onderzoek toegenomen repressie Nederland: Repressie beïnvloedt niet de mate van productie, maar wel de manier waarop dat gebeurt
11. Drugsverboden niet gebaseerd op schadelijkheid
12. Drugsverboden niet gebaseerd op verslavingspotentiaal
13. Ook niet gebaseerd op overlast
14. Drugsverboden niet gebaseerd op schadelijkheid
TTIP: How multionationals try to fool civilians, again!Matthijs Pontier
TTIP: How multionationals try to fool civilians, again!
1. TTIP? No, thanks! How big multinationals try to fool civilians, again! Matthijs Pontier, Piratenpartij
2. Problems in EU • Too much top-down • Lack of democratic control • Too much power with too many people Lobbyists’ paradise
3. TTIP: Treaty against civilians • Lobbyists behind closed doors • Protects interests multinationals • Commission: ‘TTIP = ACTA through backdoor’ • At the expense of: – Civil rights – Environment – Democracy
4. ‘Market Harmonization’ EU-US • Less restrictions on fracking • Less restrictions on GMO’s • Makes turning back privatizations impossible • Stricter copyright laws • Stricter patent laws
5. Economic Costs/Benefits TTIP • Most positive scenario EU: 0,48% economic growth in 2027 – > not even 0,05% per year!! • Costs according to OFSE report: 47 billion – Reduction trade barrier tariffs: 30 billion – Changes labour market: 10 billion – Less taxes: 7 billion
6. Capaldo (UN Global Policy Model)
7. Race to the bottom
8. Trading away our democracy • Germany: Phasing out nuclear energy (3,7bn) • Canada: Prohibition shale gas winnings (250m) • Philip Morris in Uruguay (2bn) – BBP Uruguay: 60 miljard >3%!! • Civilians pay through tax • Tribunal puts trade above everything Also above human rights and environment
9. TTIP
10. There is no planet B
11. Thanks! • @Matthijs85 • Matthijs Pontier • matthijs@piratenpartij.nl
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
How women think robots perceive them – as if robots were men
1. How women think robots perceive them – as if robots were men
Matthijs A. Pontier, Johan F. Hoorn
Center for Advanced Media Research Amsterdam (CAMeRA@VU) / The Network Institute, VU University Amsterdam
De Boelelaan 1081, 1081HV Amsterdam, The Netherlands
{m.a.pontier, j.f.hoorn}@vu.nl
Keywords: Cognitive Modeling, Emotion Modeling, Human-Computer Interaction, Turing Test, Virtual Agents
Abstract: In previous studies, we developed an empirical account of user engagement with software agents. We
formalized this model, tested it for internal consistency, and implemented it into a series of software agents to
have them build up an affective relationship with their users. In addition, we equipped the agents with a module
for affective decision-making, as well as the capability to generate a series of emotions (e.g., joy and anger). As
follow-up of a successful pilot study with real users, the current paper employs a non-naïve version of a Turing
Test to compare an agent’s affective performance with that of a human. We compared the performance of an
agent equipped with our cognitive model to the performance of a human that controlled the agent in a Wizard
of Oz condition during a speed-dating experiment in which participants were told they were dealing with a
robot in bot h conditions. Participants did not detect any differences between the two conditions in the
emotions the agent experienced and in the way he supposedly perceived the participants. As is, our model can
be used for designing believable virtual agents or humanoid robots on the surface level of emotion expression.
1. INTRODUCTION (Anabuki et al., 2000) is an agent, assisting humans
in a mixed reality space. However, none of these
applications incorporate a form of emotional
1.1. Background intelligence. Although we do not think that at this
time point computers can be emotionally intelligent,
There is a growing interest in developing we agree with Rosalind Picard, the founder of the
embodied agents and robots. They can make games field of affective computing (Picard, 1997), that a
more interesting, accommodate those who are computer can at least act emotionally intelligent.
lonely, provide health advice, make online The idea of affective computing is that
instructions livelier, and can be useful for coaching, computers ‘have’ emotions, and detect and
counseling, and self-help therapy. In extreme understand user emotions to respond appropriately
circumstances, robots can also be the better self of to the user. virtual agents who show emotions may
human operators in executing dangerous tasks. For increase the user’s likeability of a system. The
example, NASA initiated the P2P-HRI program to positive effects of showing empathetic emotions are
explore the possibility of robots and humans repeatedly demonstrated in human-human
collaborating as teammates, while perceiving each communication (e.g., Konijn & Van Vugt, 2008)
other as peers (Fong & Nourbakhsh, 2004). and are even seen as one of the functions of
Thus far, agents and social robots were mainly emotional display. Such positive effects may also
developed from a technical point of view; e.g., hold when communicating with a virtual agent.
ELIZA (Weizenbaum, 1966). The underlying model Users may feel emotionally attached to virtual
was almost lay theory; e.g., showing that smiles agents who portray emotions, and interacting with
express happiness and frowns sadness (cf. Breazeal, such “emotional” embodied computer systems may
2002). positively influence their perceptions of humanness,
Designing agents or robots is not a technical trustworthiness, and believability. A recent study
matter alone. Theories and models of human life are (Brave et al., 2005) showed that virtual agents in a
also important to explain communication rules, blackjack computer game who showed empathic
social interaction and perception, or the appraisal of emotions were rated more positively, received
certain social situations. In media psychology, greater likeability and trustworthiness, and were
mediated interpersonal communication and human- perceived with greater caring and support
computer interaction, emotions play a salient role capabilities than virtual agents not showing
and cover an important area of research. (Konijn & empathy. In addition, user frustration may be
Van Vugt, 2008). reduced if computers consider the user’s emotions
Robot designers face the challenge to create (Konijn & Van Vugt, 2008).
agents that can understand and simulate genuine Compared to human affective complexity,
affective behavior. Existing agents are often capable contemporary affective behavior of software agents
of generating meaningful responses in a and robots is still quite simple. In anticipation of
conversational setting. For example, Welbo emotionally more productive interactions between
2. user and agent, we looked at various models of Gross (2001) inspired Bosse, Pontier, & Treur
human affect-generation and affect-regulation, to (2007) to develop CoMERG (the Cognitive Model
see how affective agent behavior can be improved. for Emotion Regulation based on Gross).
Together, these approaches cover a large part of
1.2. From theories to computation appraisal-based emotion theory (Frijda, Smith &
Lazarus, Gross) and all three boil down to appraisal
Previous work described how certain models of emotion. We therefore decided to
dimensions of synthetic character design were integrate these three affect models into a model we
perceived by users and how they responded to them called Silicon Coppélia (Hoorn, Pontier & Siddiqui,
(Van Vugt, Hoorn & Konijn, 2009). A series of user 2012). Figure 1 shows Silicon Coppélia in a
studies resulted into an empirically validated graphical format.
framework for the study of user-agent interaction Silicon Coppélia consists of a loop with a
with a special focus on the explanation of user situation as input, and actions as output, leading to a
engagement and Use Intentions. The results of new situation. In this loop there are three phases:
various studies into human-agent interaction were the encoding, the comparison and the response
put together to clarify the individual contributions phase.
of an agent’s Affordances, Ethics, Aesthetics, facial In the encoding phase, the agent perceives other
Similarity, and Realism to the Use Intentions and agents (whether human or synthetic) in terms of
engagement of the human user. The interactions Ethics (good vs. bad), Affordances (aid vs.
between these agent features and their contribution obstacle), Aesthetics (beautiful vs. ugly), and
to human user’s perception of agents were Epistemics (realistic vs. unrealistic). The agent can
summarized in a schema called Interactively be biased in this perception process, because it is
Perceiving and Experiencing Fictional Characters equipped with desires that have a certain strength
(I-PEFiC). To date, this framework has an for achieving or preventing pre-defined goal-states
explanatory as well as a heuristic value because the (‘get a date’, ‘be honest’ and ‘connect well’).
extracted guidelines are important for anyone who In the comparison phase, the agent retrieves
designs virtual characters. beliefs about actions facilitating or inhibiting the
The evidence-based guidelines of I-PEFiC desired or undesired goal-states to calculate a
strengthen (e.g., ‘beauty leads to Involvement’) and general expected utility of each action. Further,
demystify (‘Ethics is more important than Realism’) agent uses certain appraisal variables, such as the
certain views regarding synthetic character design, belief that someone is responsible for
giving them experimental support. For example, not accomplishing goal-states or not. These variables
only a system’s interaction possibilities have a and the perceived features of others are appraised
direct effect on the willingness to use an agent but for Relevance (relevant or irrelevant) and Valence
the ethical behavior of the agent as a personality to the agent’s goals and concerns (positive or
does so too. Moreover, moral behaviors of the agent negative outcome expectancies).
will be checked for relevance to user’s goals and In the response phase of the model, the resulting
concerns and through that side-track, exert indirect appraisals lead to processes of Involvement and
effects on Use Intentions as well. Distance towards the other, and to the emergence of
In a simulation study (Hoorn et al., 2008), we certain Use Intentions: The agent’s willingness to
were capable of formalizing the I-PEFiC framework employ the other as a tool to achieve its own goals.
and make it the basic mechanism of how agents and Note that both overt (behavioral) and covert
robots build up affect for their human users. In (experiential) responses can be executed in this
addition, we designed a special module for affective phase. Emotions such as hope, joy, and anger are
decision-making (ADM) that made it possible for generated using appraisal variables such as the
the agent to select actions in favor or against its perceived likelihood of goal-states. The agent uses
user, hence I-PEFiCADM. an affective decision-making module to calculate
To advance I-PEFiCADM into the area of the expected satisfaction of possible actions. In this
emotion regulation even more, we also looked at module, affective influences and rational
other models of affect. Researchers at the Institute influences are combined in the decision-making
for Creative Technologies, University of Southern process. Involvement and Distance represent the
California (Gratch & Marsella, 2009) focused on affective influences, whereas Use Intentions and
different aspects of emotion generation, regulation, general expected utility represent the more rational
and affective processes. They formalized the theory influences. When the agent selects and performs an
of Emotion and Adaptation of Smith and Lazarus action, a new situation emerges, and the model
(1990) into EMA, to create agents that cope with starts at the first phase again.
negative affect. The emotion-regulation theory of
3. Figure 1: Graphical representation of Silicon Coppélia
More detailed descriptions of Silicon Coppélia We chose to confront female participants with a
and the models it is based on can be found in (Bosse male agent, because we expected that the limitations
et al., 2010; Bosse, Pontier & Treur, 2007; Gross, in richness of behavior in the experiment would be
2001; Hoorn et al., 2008; Hoorn, Pontier & Siddiqui, more easily accepted from a male agent than from a
2012; Marsella & Gratch, 2009; Pontier & Siddiqui, female one. Previous research suggests that men
2009; Van Vugt, Hoorn & Konijn, 2009). usually have more limited forms of emotional
interaction (Barret et al., 1998; Brody & Hall, 2008;
1.3. Speed-Dating as a new Turing-test Hall, 1984; Snell, Miller & Belk, 1988; 1989) and
that women are usually better equipped to do an
In previous research (e.g., Pontier & Siddiqui, emotional assessment of others (Barret et al., 1998;
2009), we performed simulation experiments to test Erwin et al., 1992; Hess et al., 2000; Levenson &
the behavior of the model. The agent behavior based Ruef, 1992; Thayer & Johnsen, 2000). By means of a
on Silicon Coppélia was consistent with the theories questionnaire, the participants diagnosed the
the model is based on, and seemed compelling emotional behavior, and the cognitive structure
intuitively. However, we tested our models in agents behind that behavior, simulated by our model, or
that interacted with other artificial agents, not with a performed by a puppeteer controlling Tom.
real user. Thus, we did not know whether the agent’s This speed-date application was the first
emotional behavior made sense to humans. confrontation of Silicon Coppélia with actual users.
Therefore, we developed a speed-dating A pilot study (Pontier, Siddiqui & Hoorn, 2010)
application as a testbed for cognitive models showed that users recognized at least certain forms of
(Pontier, Siddiqui & Hoorn, 2010). In this human affective behavior in Tom. Via a
application, the user interacted with Tom, a virtual questionnaire, users diagnosed for us how Tom
agent on a website. perceived them, and whether they recognized human-
We opted for a speed-dating application, because like affective mechanisms in Tom. Although Tom
we expected this domain to be especially useful for did not explicitly talk about it, the participants
testing emotion models. The emotionally laden recognized human-like perception mechanisms in
setting of the speed-date simplified asking the user Tom’s behavior. This finding was a first indication
what Tom would think of them, ethically, that our software had a humanoid way of assessing
aesthetically, and whether they believed the other humans, not merely other software agents.
would want to see them again, etc.
Further, in a speed-date there usually is a These promising results made us conduct a
relatively limited interaction space; also in our follow-up ‘Wizard of Oz’ (Landauer, 1987)
application, where we made use of multiple choice experiment with 54 participants. In this experiment
responses. This was done to equalize the difference we compared the performance of Tom equipped with
between a human and our model in the richness on Silicon Coppélia to the performance of a human
interaction, which was not our research objective. We controlling Tom as a puppeteer. This experiment may
wanted the difference to be based on the success or count as an advanced version of a Turing Test
failure of our human-like emotion simulations. (Turing, 1950).
4. In a Turing Test, however, participants are concerns. Like this, we created 32 (2 5) different
routinely asked whether they think the interaction emotional states in PeoplePutty; one for each
partner is a human or a robot. In this experiment, possible combination of two levels of intensity of the
however, we did not ask them so directly. After all, five simulated emotions.
because of the limited interaction possibilities of a We created a Web page for the application (see
computer interface, the behavior of Tom may not Figure 2), in which the virtual agent was embedded
seem very human-like. Therefore, all participants as a Haptek player. We used JavaScript, a scripting
would probably have thought Tom was a robot, and language, in combination with scripting commands
not a human, making it impossible to measure any provided by the Haptek software, to control the
differences. Therefore, we introduced the speed- Haptek player within the Web browser. In the middle
dating partner as a robot to see whether humans of the Web site, the affective conversational agent
would recognize human affective structures equally was shown, communicating messages through a
well in the software and the puppeteer condition. voice synthesizer (e.g., “Do you have many
Further, when testing the effect of a virtual hobbies?”) and additionally shown as text right above
interaction partner on humans, participants are the avatar. Figure 2 shows that the avatar looks
usually asked how they experience the character. In annoyed in response to the user’s reply “Well, that’s
this experiment, however, we asked people how they none of your business”.
thought the character perceived them. Thus, the During the speed-date, partners could converse
participants served as a diagnostic instrument to about seven topics: (1) Family, (2) Sports, (3)
assess the emotional behavior of Tom, and to detect Appearance, (4) Hobbies, (5) Music, (6) Food, and
for us the cognitive structure behind that behavior. (7) Relationships. For each topic, the dating partners
This way, we could check the differences between went through an interaction tree with responses that
our model and a human puppeteer in producing they could select from a dropdown box. To give an
emotional behavior, and the cognitive structure idea of what the interaction trees look like, we
responsible for that behavior. inserted the tree for Relationships in the Appendix
We hypothesized that we would not find any (Pontier & Hoorn, 2012a).
differences between the behavior of Tom controlled When the ‘start speed-date’ button above the text
by our model and that of Tom controlled by a area was pressed, Tom introduced himself and started
puppeteer, indicating the success of Silicon Coppélia by asking the user a question. The user selected an
as a humanoid model of affect generation and answer from the dropdown box below Tom. Then
regulation. This would also indicate the aptness of Tom responded and so on until the interaction-tree
the theories the model is based on. Because Silicon was traversed. When a topic was done, the user could
Coppélia is computational, this would also be very select a new topic or let Tom select one. When all
interesting for designing applications in which topics were completed, the message “the speed-
humans interact with computer agents or robots. dating session is over” was displayed and the user
was asked to fill out the questionnaire.
In the speed-dating application, Tom perceives
the user according to Silicon Coppélia (Hoorn,
2. METHOD Pontier & Siddiqui, 2012). Tom has beliefs that
features of the user influenced certain goal-states in
2.1. Participants the world. For our speed-date setting, the possible
goal-states were ‘get a date’, ‘be honest’, and
A total of 54 Dutch female heterosexual students ‘connecting well’ on each of the conversation topics.
ranging from 18-26 years of age (M=20.07, Tom has beliefs about the facilitation of these goal-
SD=1.88) volunteered for course credits or money (5 states by each possible response. Further, Tom
Euros). Participants were asked to rate their attaches a general level of positivity and negativity to
experience in dating and computer-mediated each response.
communication on a scale from 0 to 5. Participants During the speed-date, Tom updates its
communicated frequently via a computer (M = 4.02, perception of the user based on her responses during
SD = 1.00) but appeared to have little experience in the speed-date, as described in (Pontier, Siddiqui &
online dating (M = .33, SD = .80). Hoorn, 2010). The assessed Ethics, Aesthetics,
Realism, and Affordances of the user lead, while
2.2. Materials: Speed-dating application matching these aspects with the goals of Tom, to
Involvement and Distance towards the human user
We designed a speed-date application in which and a general expected utility of each action. Each
users could interact with a virtual agent, named Tom, time, Tom can select its response from a number of
to get acquainted and make an appointment. The options. The expected satisfaction of each possible
dating partner was represented by Tom, an avatar response is calculated based on the Involvement and
created in Haptek’s PeoplePutty software (Haptek). Distance towards the user and the general expected
Tom is capable of simulating five emotions: utility of the response, using the following formula:
hope, fear, joy, distress, and anger, which were
expressed through the face of the avatar with either a
low or a high intensity. This depended on little or
much relevance of user choices to Tom’s goals and
5. Figure 2: The speed-dating application.
ExpectedSatisfaction(Action) = Konijn, 2009). If the level of emotion is below a set
weu * GEU(Action) + boundary, a low intensity of the emotion is facially
wpos * (1 - abs(positivity – biasI * Involvement)) + expressed by Tom. If the level of emotion was
wneg * (1 - abs(negativity – biasD * Distance)) greater or equal than the boundary, a high intensity
of the emotion was expressed by Tom.
Tom searches for an action with the level of
positivity that comes closest to the level of 2.3. Design
Involvement, with the level of negativity closest to
the level of Distance, and with the highest expected The participants were randomly assigned to two
utility (GEU). Tom can be biased to favour positive experimental conditions. In the first condition, Tom
or negative responses to another agent. was controlled by Silicon Coppélia, whereas in the
During the speed-date, Tom simulates a series of second condition Tom was controlled by a human
emotions, based on the responses given by the user. trained to handle him (Wizard of Oz condition). All
Hope and fear are calculated each time the user participants assumed they were interacting with a
gives an answer. Hope and fear of Tom are based on robotic partner; also in the Wizard of Oz condition
the perceived likelihood that he will get a follow-up (WOz). To have some control over the
date. The joy and distress of Tom are based on idiosyncrasies of a single human controller, the
achieving desired or undesired goal-states or not. WOz condition consisted of two identical sub-
The anger of Tom is calculated using the assumed conditions with a different human puppeteer in each.
responsibility of the human user for the success of Thus, we had three conditions: (1) Tom was
the speed-date. More details about how this is done controlled by Silicon Coppélia (n=27), (2) Human
can be found in (Pontier & Siddiqui, 2010). puppeteer 1 controlled Tom (n=22), (3) Human
All five emotions implemented into the system puppeteer 2 controlled Tom (n=5). Taken together,
(i.e., hope, fear, joy, distress, and anger) are 27 participants interacted with an agent controlled
simulated in parallel (see Van Vugt, Hoorn & by a puppeteer, and 27 participants interacted with
6. an agent controlled by our model. This way, the additional items were removed, again a scale
behavior simulated by our model could be compared analysis was performed (Pontier & Hoorn, 2012a).
to behavior of the human puppeteers. In other words, All alphas, except those for Ethics and
this was an advanced kind of Turing Test where we Similarity, were between .74 and .95. The scale for
compared the cognitive-affective structure between Similarity had an alpha of .66. We removed two
conditions. In a traditional Turing Test, moreover, participants from the Ethics scale, because their
participants do not know whether they interact with scores for the items ‘Tom was trustworthy’ and
a computer or not whereas in our set-up participants ‘Tom was credible’ were extremely inconsistent.
were told they were interacting with a robot Previous studies showed that the present Ethics
throughout to avoid rejection of the dating partner scale was consistently reliable, and an important
on the basis of limited interaction possibilities. theoretical factor. Therefore, we decided to maintain
the Ethics scale despite its feeble measurement
2.4. Procedure quality. The remaining alphas and the number of
items used for the scales can be found in the
Participants were asked to take place behind a Appendix (Pontier & Hoorn, 2012a).
computer. They were instructed to do a speed-date
session with an avatar. In the WOz, the human 2.6. Statistical Analyses
controlling the avatar was behind a wall, and thus
invisible for the participants. After finishing the We performed a multivariate analysis of
speed-dating session of about 10 minutes, the variance (MANOVA) on the grand mean agreement
participants were asked to complete a questionnaire to scales, to test whether the participants perceived a
on the computer. After the experiment, participants difference in Agent-type (software vs. human
in the WOz were debriefed that they were dating an controlled). We performed paired t-tests for related
avatar controlled by a human. groups of variables.
2.5. Measures
3. RESULTS
The questionnaire consisted of 97 Likert-type
items with 0-5 rating scales, measuring agreement to
statements. Together there were 15 scales. We
designed five emotion scales for Joy, Anger, Hope, 3.1. Emotions
Fear, and Sadness, based on (Wallbot & Scherer,
1989). We also designed a scale for Situation To analyze the differences in perceived emotions
Selection, with items such as ‘Tom kept on talking in the three agent types, we performed a 3x5
about the same thing’ and ‘Tom changed the multivariate analysis of variance (MANOVA) of the
subject’, and a scale for Affective Decision-Making, between-factor Agent-type (3: Silicon Coppélia,
with items such as ‘Tom followed his intuition’ and Human1, Human2) and the within-factor of Emotion
‘Tom made rational choices’. For all eight (5: Joy, Sadness, Hope, Fear, Anger) on the grand
parameters that were present in the I-PEFiC model mean agreement score to statements. The main
(Ethics, Affordances, Similarity, Relevance, effect of Agent-type on grand mean Agreement to
Valence, Involvement, Distance, Use Intentions), the emotion scales was not significant (F(2, 51) = 1.68, p <
questions from previous questionnaires (e.g., Van .196), whereas the main effect of the Emotion factor
Vugt, Hoorn & Konijn, 2009) were adjusted and on Agreement was significant (Pillai’s Trace = .64,
reused. However, because of the different F(4, 48) = 21.59, p < .001, 2p = .64). The interaction
application domain (i.e. speed dating), and because between Agent-type and Emotions was not
the questions were now about assessing how Tom significant (Pillai’s Trace = .22, F(8, 98) = 1.545, p <
perceived the participant, and not about how the .152). More detailed results can be found in the
participant perceived Tom, we found it important to Appendix (Pontier & Hoorn, 2012a).
check the consistency of these scales again. Because the main effect of Agent-type to
A scale analysis was performed, in which items Emotion scales was not significant, this might mean
were removed until an optimal Cronbach’s alpha that there was no effect of emotion at all within a
was found and a minimum scale length of three condition. To check whether emotional behavior was
items was achieved. If removing an item only diagnosed at all by the participants, we performed a
increased Cronbach’s alpha very little, the item was one-sample t-test with 0 as the test value, equaling
maintained. After scale analysis, a factor analysis no emotions diagnosed. Results showed that all
was performed, to check divergent validity. After emotion scales differed significantly from 0. The
7. smallest t-value was found for Anger (t(2, 51) = 8.777, Tom’s perceptions of Ethics (M = 3.86, SD =
p < .001). .68) and Affordances (M = 3.78, SD = .81) in the
In addition, the significant main effect of the participant were rated the highest. His perceptions of
Emotion factor on Agreement suggested that there feeling distant towards the participant (M = 1.77, SD
were systematic differences in diagnosing emotions = .93) were rated the lowest.
in Tom, which we analyzed by paired samples t-tests In other words, the t-tests showed that
for all pairs of emotions. Out of the 10 thereby perceptions were recognized in all conditions, and
originated pairs, 6 pairs differed significantly. The 4 the MANOVA showed that participants saw equal
pairs that did not differ significantly were Joy and perceptions in humans and robots alike.
Hope (p < .444), Fear and Sadness (p < .054), Fear
and Anger (p < .908), and Sad and Anger (p < .06). 3.3. Decision-Making Behavior
Joy (M = 3.05, SD = 1.03) and Hope (M = 2.96, SD
= .82) were both recognized relatively much in Tom, To analyze the differences in perceived decision-
whereas Fear (M=1.04, SD=.80), Sad (M=.84, making behavior in the three agent-types, we
SD=.66) and Anger (M=1.02, SD=.86) were performed a 3x2 MANOVA of the between-factor
recognized little in Tom. Agent-type (3: Silicon Coppélia, Human1, Human2)
In other words, the t-tests showed that emotions and the within-factor of Decision-making behavior
were recognized in all conditions, and the (2: Affective decision making, Situation selection)
MANOVA showed that participants saw equal on the grand mean agreement score to statements.
emotions in humans and robots alike. The main effect of Agent-type on Agreement was
not significant (F < 1), whereas the main effect of
3.2. Perceptions Decision-making behavior on Agreement was small
but significant (Pillai’s Trace = .088, F(1, 51) = 4.892,
To analyze the differences in perceived p < .031, 2p = .088). The interaction between
perceptions in the three agent-types, we performed a Agent-type and Decision-making behavior was not
3x8 MANOVA of the between-factor Agent-type (3: significant (Pillai’s Trace = .04, F(2, 51) = .1.128, p <
Silicon Coppélia, Human1, Human2) and the within- .332). More detailed results can be found in the
factor of Perception (8: Ethics, Affordances, Appendix (Pontier & Hoorn, 2012a).
Relevance, Valence, Similarity, Involvement, Because the main effect of Agent-type to
Distance, Use Intentions) on the grand mean Decision-making behavior scales was not
agreement score to statements. The main effect of significant, this might mean that there was no effect
Agent-type on Agreement to perception scales was of Decision-making behavior at all within a
not significant (F < 1), whereas the main effect of condition. To check whether decision-making
the Perception factor on Agreement was significant behavior was diagnosed at all by the participants, we
(Pillai’s Trace = .87, F(7, 43) = 39.63, p < .001, 2p = performed a one-sample t-test with 0 as the test
.87). The interaction between Agent-type and value, equaling no decision-making behavior
Perception was not significant (Pillai’s Trace = .18, diagnosed. Results showed that both Situation
F(14, 88) = .635, p < .828). More detailed results can selection (t(2, 51) = 14.562, p < .001) and Affective
be found in the Appendix (Pontier & Hoorn, 2012a). decision-making (t(2, 51) = 15.518, p < .001) both
Because the main effect of Agent-type to differed significantly from 0.
Perception scales was not significant, this might In addition, the significant main effect of the
mean that there was no effect of perception at all Perception factor on Agreement suggested that there
within a condition. To check whether the were systematic differences in diagnosing
perceptions of Tom were diagnosed at all by the perceptions in Tom, which we analyzed by paired
participants, we performed a one-sample t-test with samples t-test for affective decision-making (M =
0 as the test value, equaling no perceptions 2.24, SD = 1.07) and situation selection (M = 1.91,
diagnosed. Results showed that all perception scales SD = 1.32). The pair differed significantly (t(53) =
differed significantly from 0. The smallest t-value 1.776, p < .081).
was found for Distance (t(2, 51) = 15.865, p < .001). In other words, the t-tests showed that decision-
In addition, the significant main effect of the making behavior was recognized in all conditions,
Perception factor on Agreement suggested that there and the MANOVA showed that participants saw
were systematic differences in diagnosing equal decision-making behavior in humans and
perceptions in Tom, which we analyzed by paired robots alike.
samples t-tests for all pairs of perceptions. Out of the
28 thereby originated pairs, 23 pairs differed
significantly. The pair that differed the most was
Ethics and Distance (t(51) = 13.59, p < .001).
8. 4. DISCUSSION and advice systems, or coach and therapist systems.
For example, Silicon Coppélia could be used to
improve the emotional intelligence of a ’virtual
4.1. Conclusion crook‘ that could be used for police studies to
practice situations in which the police officers
In this paper, we equipped a virtual agent with should work on the emotions of the crook, for
Silicon Coppélia (Hoorn, Pontier & Siddiqui, 2012), example questioning techniques (Hochschild, 1983).
a cognitive model of perception, affection, and Another possible use of models of human processes
affective decision-making. As an advanced, implicit is in software and/or hardware that interacts with a
version of a Turing Test, we let participants perform human and tries to understand this human‘s states
a speed-dating session with Tom, and asked them and processes and responds in an intelligent manner.
how they thought Tom perceived them during the Many ambient intelligence systems (e.g., Aarts,
speed-date. What the participants did not know, was Hartwig & Schuurmans, 2001) include devices that
that in one condition, a human was controlling Tom, monitor elderly persons. In settings where humans
whereas in the other condition, Tom was equipped interact intensively with these systems, such as
with Silicon Coppélia. cuddle bots for dementia patients (e.g., Nakajima et
A novel element in this experiment was that al, 2001), the system can combine the data gathered
participants were asked to imagine how an agent from these devices with Silicon Coppélia to maintain
perceives them. To our knowledge there does not a model of the emotional state of the user. This can
exist previous research in which participants were enable the system to adapt the type of interaction to
asked to assess the perceptions of an artificial other. the user‘s needs.
It is a nice finding, that the scales of I-PEFiC (Van Silicon Coppelia can also be used to improve
Vugt, Hoorn & Konijn, 2009), which were originally self-help therapy. Adding the moral reasoning
used to ask how participants perceived an interactive system will be very important for that matter.
agent, could be used quite well to ask participants Humans with psychological disorders can be
how they thought Tom perceived them. supported through applications available on the
The results showed that, even in this enriched Internet and virtual communities of persons with
and elaborated version of the classic Turing Test, in similar problems.
the year of the Turing Centenary, participants did New communication technologies have led to an
not detect differences between the two versions of impressive increase of self-help programs that are
Tom. Not that the variables measured by the delivered through the Internet (e.g., Christensen,
questionnaire did not have any effect; the effects just Griffiths & Jorm, 2004; Clarke et al., 2002; Spek et
did not differ. Thus, within the boundaries of limited al., 2007). Several studies concluded that self-help
interaction possibilities, the participants felt the therapies can be more efficient in reducing mental
puppeteer and our software perceived that their health problems, and less expensive than traditional
moral fiber was the same, their relevance the same, therapy (e.g., Anderson et al., 2005; Andrews,
and so on. The participants felt the puppeteer and Henderson & Hall, 2001; Apodaca & Miller, 2003;
our software were equally eager to meet them again, Bijl & Ravelli, 2000; Cuijpers, 1997; Den Boer,
and exhibited equal ways to select a situation and to Wiersma & Van den Bosch, 2004; Griffiths et al.,
make affective decisions. Also, the emotions the 2006; Spek et al., 2007).
participants perceived in Tom during the speed-date Web-based self-help therapy can be a solution
session did not differ between conditions. Emotion for people who would otherwise not seek help,
effects could be observed by the participants, and wishing to avoid the stigma of psychiatric referral or
these effects were similar for a human controlled to protect their privacy (Williams, 2001). The
agent and software agent alike. This is a nice finding majority of persons with a mental disorder in the
for the engineer who wants to use these models for general population do not receive any professional
application development, such as the design of mental health services (an estimated 65%)
virtual agents or robots. After all, on all kinds of (Andrews, Henderson & Hall, 2001; Bijl & Ravelli,
facets, participants did not experience any difference 2000). In many occupations, such as the police
between the expression of human behavior and force, the fire service and farming, there is much
behavior generated by the model. stigma attached to receiving psychological
treatment, and the anonymity of Web-based self-
help therapy would help to overcome this (Peck,
4.2. Applications 2007). Also many other people feel a barrier to seek
help for their problems through regular health-care
This can be of great use in many applications, systems; e.g., in a study by Spek et al. (Spek et al.,
such as (serious) digital games, virtual stories, tutor 2007) about internet-based cognitive behavioral
9. therapy for sub-threshold depression for people over
50 years old, many participants reported not seeking
help through regular health-care systems because
they were very concerned about being stigmatized.
Patients may be attracted to the idea of working on
their own to deal with their problems, thereby
avoiding the potential embarrassment of formal
psychotherapy (Williams, 2001).
Further, self-help therapy is particularly suited to
remote and rural areas, where the population may be
so small that ready access to a face-to-face therapist
in all communities cannot be economically justified.
Even when face-to-face therapy is available, both
patients and therapists often need to travel long
distances to a clinic. Self-help therapy may also be Figure 3. The cuddly toy chimpanzee robot we plan to
useful in unusual environments such as oilrigs and equip with Silicon Coppélia in future research
prisons, where face-to-face therapy is not normally
available. Self-help therapy can also be offered to 2012b). In this system, actions are evaluated against
patients while they are on a waiting list, with the a number of moral principles. When integrating the
option to receive face-to-face therapy later, if two systems, moral goals could be treated the same
required (Peck, 2007) as other goals that motivate the robot’s behavior. In
Self-help therapy may be even more successful entertainment settings, we often like characters that
when the interface is enhanced or replaced by a are naughty (Konijn & Hoorn, 2005). In
robot therapist that has Silicon Coppélia installed. entertainment, morally perfect characters may even
The anonymity of robot-supported self-help therapy be perceived as boring. In Silicon Coppélia (Hoorn,
could overcome potential embarrassment of Pontier & Siddiqui, 2011), this could be
undergoing formal treatment (cf. Rochlen, Zack, & implemented by updating the affective decision
Speyer, 2004). When regular therapy puts up too making module. Morality would be added to the
high a threshold, a robot therapist is less threatening, other influences that determine the Expected
what the patient reveals is inconsequential, the Satisfaction of an action in the decision making
patient is in control, and all in all, interaction with process. By doing so, human affective decision-
the virtual therapist has a “dear diary” effect. As if making behavior could be further explored.
you were speed-dating with a real partner.
Acknowledgements
4.3. Future Research This study is part of the SELEMCA project
within CRISP (grant number: NWO 646.000.003).
In future research, we will test an extended We would like to thank Adriaan Hogendoorn and
version of the current model using toy chimpanzees Benjamin van Gelderen for their help with the
that play a simple tic-tac-toe game with the user. statistical analysis. Furthermore, we would like to
Based on whether the agent reaches its goals thank Marco Otte for his help in conducting the
(winning and losing when the agent has ambitions to experiment. Finally, we would like to thank Elly
win or lose), the likelihood of these goals, and the Konijn for some fruitful discussions.
expectedness of the move of the user and the
outcome of a game, the emotions joy, distress, hope,
fear and surprise are simulated and shown by the
agent by means of bodily expressions. The
REFERENCES
chimpanzee (see Figure 3) will be able to trade
rational choices to win the game for affective Aarts, E., Harwig, R., and Schuurmans, M., 2001.
choices to let the human opponent win if she is nice Ambient Intelligence. In The Invisible Future: The
to him. For doing this, we have equipped toy chimps Seamless Integration of Technology into Everyday
with several sensors so users can touch and pet the Life, McGraw-Hill, 2001.
Anabuki, M., Kakuta, H., Yamamoto, H., & Tamura, H.,
chimp to show affection. These inputs will be used
2000. Welbo: An Embodied Conversational Agent
to control the affect of the chimp towards the user
Living in Mixed Reality Space. In Proceedings of the
and provides an inspirational testbed for our model Conference on Human Factors in Computing Systems
simulations. (CHI 2000), Extended Abstracts, The Hague, The
Additionally, we will integrate Silicon Coppélia Netherlands, pp. 10-11.
with a moral reasoning system (Pontier & Hoorn,
10. Anderson, L., Lewis, G., Araya, R., Elgie, R., Harrison, Clarke, G., Reid, E., Eubanks, D., O'Connor, E., DeBar, L.
G., Proudfoot, J., Schmidt, U., Sharp, D., Weightman, L., Kelleher, C., Lynch, F., & Nunley, S., 2002.
A., & Williams, C., 2005. Self-help books for Overcoming depression on the Internet (ODIN): a
depression: how can practitioners and patients make randomized controlled trial of an Internet depression
the right choice? British Journal of General Practice, skills intervention program. In Journal of Medical
Vol. 55(514), pp. 387-392 Internet Research, Vol. 4, No. 3: e14.
Andrews, G., Henderson, S., & Hall, W., 2001. Cuijpers, P., 1997. Bibliotherapy in unipolar depression, a
Prevalence, comorbidity, disability and service meta-analysis. In Journal of Behavior Therapy &
utilisation. Overview of the Australian National Experimental Psychiatry, Vol. 28, No. 2, pp. 139-147
Mental Health Survey. In The British Journal of Erwin, R.J. Gur, R.C., Gur, R.E., Skolnick, B.,
Psychiatry, Vol. 178, No. 2, pp. 145-153. Mawhinney-Hee, M., & Smailis, J., 1992. Facial
Apodaca, T. R., & Miller, W. R., 2003. A meta-analysis of emotion discrimination: I. Task construction and
the effectiveness of bibliotherapy for alcohol behavioral findings in normal subjects, In Psychiatry
problems. In Journal of Clinical Psychology, Vol. 59, Research, Vol. 42, Issue 3, pp. 231-240.
No. 3, pp. 289-304. Fong, T., & Nourbakhsh, I., 2004. Peer-to-peer human-
Barrett, L.F., Robin, L., Pietromonaco, P.R., Eyssell, robot interaction for space exploration. AAAI Fall
K.M., 1998. Are Women The "More Emotional Sex?" Symposium.
Evidence from Emotional Experiences in Social Griffiths, F., Lindenmeyer, A., Powell, J., Lowe, P., &
Context. In Cognition and Emotion, vol. 12, pp. 555- Thorogood, M., 2006. Why Are Health Care
578. Interventions Delivered Over the Internet? A
Bijl, R. V., & Ravelli, A., 2000. Psychiatric morbidity, Systematic Review of the Published Literature,
service use, and need for care in the general Journal of Medical Internet Research, Vol. 8, No. 2:
population: results of The Netherlands Mental Health e10.
Survey and Incidence Study. In American Journal of Gross, J.J., 2001. Emotion Regulation in Adulthood:
Public Health, Vol. 90, Iss. 4, pp. 602-607. Timing is Everything. In Current Directions in
Boer, den, P. C. A. M., Wiersma, D., & Bosch, van den, Psychological Science, 10(6), pp. 214-219.
R. J., 2004. Why is self-help neglected in the treatment Hall, J.A., 1984. Nonverbal sex differences:
of emotional disorders? A meta-analysis. In Communication accuracy and expressive style.
Psychological Medicine, Vol. 34, No. 6, pp. 959-971. Baltimore: Johns Hopkins University Press.
Bosse, T., Gratch, J., Hoorn, J.F., Pontier, M.A., Siddiqui, Haptek, Inc., http://www.haptek.com
G.F., 2010. Comparing Three Computational Models Hess, U., Senécal, S., Kirouac, G., Herrera, P., Philippot,
of Affect. In Proceedings of the 8th International P., Kleck, R.E., 2000. Emotional expressivity in men
Conference on Practical Applications of Agents and and women: Stereotypes and self-perceptions, In
Multi-Agent Systems, PAAMS, pp. 175-184. Cognition and Emotion, Vol. 14, pp. 609—642.
Bosse, T., Pontier, M.A., Treur, J., 2007. A Dynamical Hochschild, A. R., 1983. The managed heart. Berkeley:
System Modelling Approach to Gross´ Model of University of California Press.
Emotion Regulation. In Lewis, R.L., Polk, T.A., & Hoorn, J.F., Pontier, M.A., Siddiqui, G.F., 2008. When the
Laird, J.E. (eds.), In Proceedings of the 8th user is instrumental to robot goals. First try: Agent
International Conference on Cognitive Modeling, uses agent. In Proceedings of IEEE/WIC/ACM Web
ICCM'07, pp. 187--192, Taylor and Francis. Intelligence and Intelligent Agent Technology, WI-
Brave, S., & Nass C., 2002. Emotion in human-computer IAT ‘08, IEEE/WIC/ACM, Sydney AU, pp. 296-301.
interaction. In J. Jacko, & A. Sears (Eds.), Handbook Hoorn, J.F., Pontier, M.A., & Siddiqui, G.F., 2012.
of Human-Computer Interaction. Mahwah, NJ: Coppélius' Concoction: Similarity and
Lawrence Erlbaum Associates, pp. 251–271. Complementarity Among Three Affect-related Agent
Brave, S., Nass, C., & Hutchinson, K., 2005. Computers Models. Cognitive Systems Research Journal, 2012,
that care: investigating the effects of orientation of pp. 33-49.
emotion exhibited by an embodied computer agent. JavaScript – MDC,
International Journal of Human-Computer Studies, http://developer.mozilla.org/en/docs/About_JavaScript
62(2), pp. 161–178. Konijn, E.A., & Hoorn, J.F., 2005. Some like it bad.
Breazeal, C., 2002. Designing Sociable Robots, MIT Testing a model for perceiving and experiencing
Press, Cambridge, MA. fictional characters. Media Psychology, 7(2), 107-144.
Brody, L.R., & Hall, J.A., 2008. Gender and Emotion in Konijn, E. A., & Van Vugt, H. C., 2008. Emotions in
Context. In M. Lewis and J.M. Haviland-Jones, L. F. mediated interpersonal communication. Toward
Barrett (Eds.), Handbook of emotions, pp. 395-408. modeling emotion in virtual agents. In Konijn, E.A.,
New York: Guilford Press. Utz, S., Tanis, M., & Barnes, S.B. (Eds.). Mediated
Christensen, H., Griffiths, K. M., & Jorm, A. F., 2004. Interpersonal Communication. New York: Taylor &
Delivering interventions for depression by using the Francis group/ Routledge.
internet: randomised controlled trial. In BMJ, Vol. Landauer, T.K., 1987. Psychology as a mother of
328, pp. 265-269. invention. Proceedings of the SIGCHI/GI conference
11. on Human factors in computing systems and graphics emotional disclosures: The impact of disclosure
interface, pp. 333-335. recipient, culture, and the masculine role. In Sex Roles,
Levenson, R.W., & Ruef, A.M., 1992. Empathy: A 21, 467-485.
Physiological Substrate, Journal of Personality and Spek, V., Cuijpers, P., Nyklíĉek, I., Riper, H., Keyzer, J.,
Social Psychology, Volume 63, Issue 2, August 1992, & Pop, V., 2007. Internet-based cognitive behavior
Pages 234-246. therapy for emotion and anxiety disorders: a meta-
Marsella, M., Gratch, J., 2009. EMA: A Process Model of analysis. In Psychological Medicine, Vol. 37, pp. 1-10
Appraisal Dynamics. In Journal of Cognitive Systems Thayer, J. and Johnsen, B. H., 2000. Sex differences in
Research, Vol. 10, No. 1, pp. 70-90. judgement of facial affect: A multivariate analysis of
Nakajima, K., Nakamura, K., Yonemitsu, S., Oikawa, D., recognition errors. In Scandinavian Journal of
Ito, A., Higashi, Y., Fujimoto, T., Nambu, A., Psychology, 41: 243–246.
Tamura, T., 2001. Animal-shaped toys as therapeutic Turing, A.M., 1950. Computing Machinery and
tools for patients with severe dementia. Engineering in Intelligence. Mind 54(236), pp. 433-460.
Medicine and Biology Society. In Proceedings of the Van Vugt, H.C., Hoorn, J.F., & Konijn, E.A., 2009.
23rd Annual International Conference of the IEEE, Interactive engagement with embodied agents: An
vol. 4, 3796-3798. empirically validated framework. In Computer
Peck, D., 2007. Computer-guided cognitive–behavioral Animation and Virtual Worlds, Vol. 20, pp. 195-204
therapy for anxiety states. Emerging areas in Anxiety, Wallbot, H.G., & Scherer, K.R., 1989. Assessing Emotion
by Questionnaire. Emotion Theory, Research and
Vol. 6, No. 4, pp. 166-169.
Experience, Vol. 4, pp. 55-82.
Picard, R. W., 1997. Affective Computing. Cambridge,
Weizenbaum, J., 1966 ELIZA - A computer program for
MA: MIT Press.
the study of natural language communications
Pontier, M.A., & Hoorn, J.F., 2012a. Appendix. between men and machines. In Communications of the
http://camera-vu.nl/matthijs/SpeeddateAppendix.pdf Association for Computing Machinery, 9, pp. 36-45.
Pontier, M.A., & Hoorn, J.F., 2012b. Toward machines Williams, C., 2001. Use of Written Cognitive-Behavioral
that behave ethically better than humans do. In Therapy Self-Help Materials to treat depression. In
Proceedings of of the 34th International Annual Advances in Psychiatric Treatment, Vol. 7, pp. 233–
Conference of the Cognitive Science Society, 240.
CogSci'12, pp. 2198-2203.
Pontier, M.A., Siddiqui, G.F., 2009. Silicon Coppélia:
Integrating Three Affect-Related Models for
Establishing Richer Agent Interaction. In WI-IAT,
IEEE/WIC/ACM International Conference on Web
Intelligence and Intelligent Agent Technology, vol. 2,
pp. 279—284.
Pontier, M.A., Siddiqui, G.F., & Hoorn, J., 2010. Speed-
dating with an Affective Virtual Agent - Developing a
Testbed for Emotion Models In: J. Allbeck et al.
(Eds.), In Proceedings of the 10th International
Conference on Intelligent Virtual Agents, IVA'10,
Lecture Notes in Computer Science, Vol. 6356, pp.
91-103.
Reeves, B., & Nass, C., 1996. The Media Equation: How
People Treat Computers, Television, and New Media
Like Real People and Places. New York: Cambridge
University Press.
Rochlen, A.B., Zack, J.S., & Speyer, C. (2004). Online
therapy: Review of relevant definitions, debates, and
current empirical support. In Journal of Clinical
Psychology, 60, 3, 269-283.
Smith, C. A., & Lazarus, R. S., 1990. Emotion and
adaptation. In L. A. Pervin (Ed.), Handbook of
personality: Theory & research, (pp. 609-637). NY:
Guilford Press.
Snell, W.E., Miller, R.S., & Belk, S.S., 1988.
Development of the Emotional Self-Disclosure Scale.
In Sex Roles, 18, 467-485.
Snell, W.E., Miller, R.S., Belk, S.S., Garcia-Falconi, R., &
Hernandez-Sanchez, J.E., 1989. Men’s and women’s