This slide shows (1) AI and Accountability , (2) AI Ethics, (2) Privacy Protection. Several AI ethics documents such as IEEE EAD, EC-HELG Ethics Guideline for Trustworthy AI, Social Principles of Human-Centric AI(Japan), focus on AI's transparency, accountability and trust. We follow the discussions of these documents around the above (1),(2) and (3) topics.
What is Accountability of AI? We answer to this question by clarifying responsibility, explainability and liability of limited autonomous AI with several bright and dark real examples.
Then we move to the concept of "Trust " which is of not limited to single AI system but group AI ‘s behavior.
Artificial Intelligence: What Is Reinforcement Learning?Bernard Marr
Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses. In this SlideShare, I want to provide a simple guide that explains reinforcement learning and give you some practical examples of how it is used today.
The Impact of Artificial Intelligence on the Built EnvironmentJames Dearsley
As part of an event organised by RICS, Chris Hoar gave a great insight into the world of AI in the Facilities Management sector and this is the presentation that he gave.
For more information on this event please visit www.jamesdearsley.co.uk
This slide shows (1) AI and Accountability , (2) AI Ethics, (2) Privacy Protection. Several AI ethics documents such as IEEE EAD, EC-HELG Ethics Guideline for Trustworthy AI, Social Principles of Human-Centric AI(Japan), focus on AI's transparency, accountability and trust. We follow the discussions of these documents around the above (1),(2) and (3) topics.
What is Accountability of AI? We answer to this question by clarifying responsibility, explainability and liability of limited autonomous AI with several bright and dark real examples.
Then we move to the concept of "Trust " which is of not limited to single AI system but group AI ‘s behavior.
Artificial Intelligence: What Is Reinforcement Learning?Bernard Marr
Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses. In this SlideShare, I want to provide a simple guide that explains reinforcement learning and give you some practical examples of how it is used today.
The Impact of Artificial Intelligence on the Built EnvironmentJames Dearsley
As part of an event organised by RICS, Chris Hoar gave a great insight into the world of AI in the Facilities Management sector and this is the presentation that he gave.
For more information on this event please visit www.jamesdearsley.co.uk
HUMAN RIGHTS IN THE AGE OF ARTIFICIAL INTELLIGENCE. This report is a product of Access Now. We thank lead author Lindsey Andersen for her
significant contributions. If you have questions about this report or you would like more information, you can contact info@accessnow.org.
Sldies I proposed for the lecture at CUOA Business School for the 2016 Edition of the Executive Master on ITC - Path Big Data e Social Analytics - http://www.cuoa.it/ita/formazione/corsi-executive/jobleader-big-data-e-social-analytics.php#/
This presentation was after a showing of Robot & Frank and Livermore, CA public library. The point of the session was to explore AI basics and discuss the potential of the movie coming true.
Artificial Intelligence is trendy. Every event, every strategy meeting and every consulting firm talks about it. This whitepaper aims to separate actual facts and important background information from the overarching marketing buzz.
You will get a short but information-rich wrap up about: What causes the current hype? Where are we today? What are the innovation leaders doing with AI? And what are immediate action points to focus on by applying artificial intelligence to your business?
The article illustrates the key vital elements of artificial Intelligence impacts and effects on the corporate domain in perspective. It contemplates the issues and problems and also suggestive solutions in brief..
It serves as a guideline to the corporate mangers to get the insights into the latest research findings
Unravel COVID-19 From a Systems Thinking LensNUS-ISS
COVID-19 pandemic has exposed the gaps in every countries' infrastructure and society. As we deal with one threat of the crisis, we are quickly overwhelmed by secondary consequences. The butterly effect of COVID-19 unveils the reality of system interdependence at multiple levels. Join us in understanding the complex nature of this interdependence through the lens of system thinking and discuss how might we manage this crisis together with fresh eyes.
Social Effects by the Singularity -Pre-Singularity Era-Hiroshi Nakagawa
Contents:
Stance of scientists community against Pre-Singularity problems
Amplification vs. Replacement
AI takes over jobs
Boarder line between amplification and replacement
Autonomous driver: trolley problem
The right to be forgotten
Towards black box
Responsibility
Vulnerability of financial dealing system made of many AI agent traders connected via internet
AI and weapon
Filter bubble phenomena
Analogy: Selfish gene
AI and privacy
The right to be forgotten, Profiling and Don’t Track
Feeling of friendliness to android
Again self conscious and identity
HUMAN RIGHTS IN THE AGE OF ARTIFICIAL INTELLIGENCE. This report is a product of Access Now. We thank lead author Lindsey Andersen for her
significant contributions. If you have questions about this report or you would like more information, you can contact info@accessnow.org.
Sldies I proposed for the lecture at CUOA Business School for the 2016 Edition of the Executive Master on ITC - Path Big Data e Social Analytics - http://www.cuoa.it/ita/formazione/corsi-executive/jobleader-big-data-e-social-analytics.php#/
This presentation was after a showing of Robot & Frank and Livermore, CA public library. The point of the session was to explore AI basics and discuss the potential of the movie coming true.
Artificial Intelligence is trendy. Every event, every strategy meeting and every consulting firm talks about it. This whitepaper aims to separate actual facts and important background information from the overarching marketing buzz.
You will get a short but information-rich wrap up about: What causes the current hype? Where are we today? What are the innovation leaders doing with AI? And what are immediate action points to focus on by applying artificial intelligence to your business?
The article illustrates the key vital elements of artificial Intelligence impacts and effects on the corporate domain in perspective. It contemplates the issues and problems and also suggestive solutions in brief..
It serves as a guideline to the corporate mangers to get the insights into the latest research findings
Unravel COVID-19 From a Systems Thinking LensNUS-ISS
COVID-19 pandemic has exposed the gaps in every countries' infrastructure and society. As we deal with one threat of the crisis, we are quickly overwhelmed by secondary consequences. The butterly effect of COVID-19 unveils the reality of system interdependence at multiple levels. Join us in understanding the complex nature of this interdependence through the lens of system thinking and discuss how might we manage this crisis together with fresh eyes.
Social Effects by the Singularity -Pre-Singularity Era-Hiroshi Nakagawa
Contents:
Stance of scientists community against Pre-Singularity problems
Amplification vs. Replacement
AI takes over jobs
Boarder line between amplification and replacement
Autonomous driver: trolley problem
The right to be forgotten
Towards black box
Responsibility
Vulnerability of financial dealing system made of many AI agent traders connected via internet
AI and weapon
Filter bubble phenomena
Analogy: Selfish gene
AI and privacy
The right to be forgotten, Profiling and Don’t Track
Feeling of friendliness to android
Again self conscious and identity
Ethical Dimensions of Artificial Intelligence (AI) by Rinshad ChoorapparaRinshad Choorappara
Explore the ethical landscape of Artificial Intelligence (AI) through our insightful PowerPoint presentation. Delve into crucial considerations that shape the responsible development and deployment of AI technologies. From privacy concerns and bias mitigation to transparency and accountability, this presentation covers the key ethical dimensions of AI. Gain a comprehensive understanding of the ethical challenges and solutions in the rapidly evolving world of artificial intelligence. Stay informed and empower your audience with the knowledge needed to navigate the ethical intricacies of AI responsibly.
Let us see the good and bad effects of the impact of Artificial Intelligence and the emerging technologies!
Artificial Intelligence (AI) is one of the hottest topics in the tech and startup world at the moment. The field of AI and its associated technologies present a range of opportunities – as well as challenges – for corporates. Learn more about what Artificial Intelligence means for your organization.
This guide demystifies AI and democratizes AI knowledge on how it creates and delivers value. It provides an essential understanding of AI to anyone with varying technical knowledge, curiosity, and interest in the technology.
20240104 HICSS Panel on AI and Legal Ethical 20240103 v7.pptxISSIP
20240103 HICSS Panel
Ethical and legal implications raised by Generative AI and Augmented Reality in the workplace.
Souren Paul - https://www.linkedin.com/in/souren-paul-a3bbaa5/
Event: https://kmeducationhub.de/hawaii-international-conference-on-system-sciences-hicss/
What really is Artificial Intelligence about? Harmony Kwawu
AI systems are growing. But what is AI, where did the idea behind it come from, what is intelligence, how does expert level intelligence work, and perhaps most importantly, would AI systems eventually make human beings redundant?
Intelligence: “The capacity to learn and solve problems.”
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
Till now we have discussed in brief about Artificial Intelligence.
We have discussed some of its principles, its applications, its achievements etc.
The ultimate goal of institutions and scientists working of AI is to solve majority of the problems or to achieve the tasks which we humans directly can’t accomplish.
It is for sure that development in this field of computer science will change the complete scenario of the world. Now it is the responsibility of creamy layer of engineers to develop this field.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
Presentación sobre inteligencia artificial. Los neofito dueños del sitio quieren hacerse millonarios a costa del esfuerzo de los usuarios. Lo malo es que no le dan ni un solo peso al usuario que sube sus trabajos. Sin embargo ellos les cobra a los demás U$s 100 por mes.
K-anonymization has been regarded as a great method to make a bad person indistinguishable among k people whose quasi identifiers are same.
It, unfortunately, has a problematic side effect of defamation. In this case, defamation means the case where other good k-1 people are suspected as a bad person because both of a bad person and good people have the same quasi identifiers because of k-anonymization. This slide shows a mathematical model of defamation and proposes an algorithm which minimizes the probability of defamation.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
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
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
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:
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
A tale of scale & speed: How the US Navy is enabling software delivery from l...
AI Forum-2019_Nakagawa
1. AI and Ethics and Accountability
Hiroshi Nakagawa
(RIKEN AIP)
Images in this file is licensed by creative commons via power point of MicroSoft.
2019/4/26
1
2. Contents
• Amplification vs. Replacement
• AI takes over jobs
• Misuse/Abuse of AI
• AI Ethics
• Transparency, accountability, trust
• AI weapon
• Flush Crash
3. Reference
• Ray Kurzweil: The Singularity is Near ,Loretta
Barrett Books Inc.2005
• Nick Bostrom: Superintelligence, Oxford
University Press. 2014
• James Barrat:Our Final Invention: Artificial
Intelligence and the End of the Human Era, New
York Journal of Books, 2013
• John Markoff: Machines of Loving Grace: The
Quest for Common Ground Between Humans and
Robots ,2015
• Thomas H. Davenport , Julia Kirby :Only Humans
Need Apply: Winners and Losers in the Age of
Smart Machines , Harper Business, 2016
4. Amplify vs. Replace
• Does Artificial Intelligence amplify human
competence?
– IA: Intelligent Assistance/ Intelligence Amplifier
• Does Artificial Intelligence replace human?
– AI: Artificial Intelligence
• “IA vs AI” is a basic view of Markoff’s book: Machines of
Loving Grace: Chapter 4 beautifully describes this view
and the history of AI.
5. Amplify vs. Replace
• IA vs. AI : This is a 60 year old hostile or
complementary relation since the starting time of
AI.
– When AI is booming, IA is low profile, or vice versa.
– If AI technology becomes stuck, researchers become
favorite IA, such as Terry Winograd Larry
Page and Sergey Brin Google
– Like this example, IA has provided us with much much
great infuluencial technologies and tools than AI has.
– Question: Is Deep Learning IA or AI? yes. IA
6. Is a human in the loop or out of the loop?
• Designing principle parallel with “IA vs. AI” is “a human is in
the loop or out of the loop .”
• In the loop IA
– A system is an extension of human abilities. A human being and
a system do work collaboratively.
– A human does some task being aided by an AI based system.
– A human might not understand what is going on in an AI system.
• Out of the loop AI
– A system acts autonomously. A human only commands a system.
– A human is no more an actual stakeholder.
– A human is usually living in a very easy position,
but could not get by when some thing happens.
7. Is a human in the loop or out of the loop?
• In the loop IA
• Out of the loop AI
• Generally speaking, the problem is to find the criteria of
how far AI system should or can be done on behalf of
human beings.
– Of course, the remaining is to be done by a human being.
• This is a traditional problem between machine and
human, but becomes complicated in the era of AI.
• To leave all the decision-making to AI is too easy for
a human being but ends up with a kind of addiction
and intelligently atrophic(shrinking) .
9. Davenport says we can find new jobs
immune to AI invasion, but…
–Jobs aiming at higher quality than AI, say
judgment without data
It must be done by so called genius?
10. Davenport says we can find new jobs
immune to AI invasion, but…
–Jobs AI can not do such as
human
intercommunication,
persuasion, etc..
AI knows more and precise
about things, event, etc.
If NLP becomes great , AI is
more relied by human…
AI
AI
11. Davenport says we can find new jobs
immune to AI invasion, but…
–Jobs of finding
connection between
business and technology
AI can do more thorough
investigation about
linkage between
business and technology
than human.
AI
12. Davenport says we can find new jobs
immune to AI invasion, but…
–Jobs less economical with employing
machine or AI , in other words, quite rare
and case specific task.
AI
AI
AI has a learning ability, so easy to
cope with rare or special task!
13. Davenport says we can find new jobs
immune to AI invasion, but…
–Jobs of developing AI
However, AI itself will develop AI in
the near future!
AI
AI
AI
14. Davenport says we can find new jobs
immune to AI invasion, but…
–Jobs of explaining
the results
generated by AI
or action done by
AI.
Again, however,
it will only be
done by AI!
AI Explanation
17. How to cope with this situation?
– Basic income ….
no incentive,
Unmotivated…
If we want to a
hobby, it needs
amount of money…
18. If a human job is completely replaced by AI and
a human being is outside of job process
AI
Good-
by
I forgot how to
do this job!
I could not!
Skills are lost
forever!
deleted or
out-of date,
etc.
20. One of the real problem is
Misuse/Abuse of AI
20
21. IEEE Ethically Aligned Design version 2
1. Executive Summary
2. General Principles
3. Embedding Values Into Autonomous
Intelligent Systems
4. Methodologies to Guide Ethical Research
and Design
5. Safety and Beneficence of Artificial General
Intelligence (AGI) and Artificial
Superintelligence (ASI)
6. Personal Data and Individual Access
Control
7. Reframing Autonomous Weapons Systems
8. Economics/Humanitarian Issues
9. Law
10. Affective Computing
11. Classical Ethics in Artificial Intelligence
12. Policy
13. Mixed Reality
14. Well-being 21
The final version was published
22. IEEE EAD (Final) on April 2019
• 1. Human Rights
– A/IS shall be created and operated to respect, promote,
and protect internationally recognized human rights.
• 2. Well-being
– A/IS creators shall adopt increased human well-being
as a primary success criterion for development.
• 3. Data Agency
– A/IS creators shall empower individuals with the ability
to access and securely share their data, to maintain
people’s capacity to have control over their identity.
• 4. Effectiveness
– A/IS creators and operators shall provide evidence of
the effectiveness and fitness for purpose of A/IS.
22
23. IEEE EAD (Final) on April 2019
• 5. Transparency
– The basis of a particular A/IS decision should always be
discoverable.
• 6. Accountability
– A/IS shall be created and operated to provide an
unambiguous rationale for all decisions made.
• 7. Awareness of Misuse
– A/IS creators shall guard against all potential misuses
and risks of A/IS in operation.
• 8. Competence
– A/IS creators shall specify and operators shall adhere to
the knowledge and skill required for safe and effective
operation. 23
24. It’s not me but AI says so!
AIA society without
freedom of speech
nor even human
rights.
We need to design
the society in which
we have the right to
object AI’s decision.
GDPR article 22
Why
me?
25. GDPR article 22:
Automated individual decision-making,
including profiling
• 1. The data subject shall have the right not to
be subject to a decision based solely on
automated processing, including profiling,
which produces legal effects concerning him
or her or similarly significantly affects him or
her
25
26. IEEE EAD version2
How to cope with misuse/abuse of AI
– Find out misuse/abuse of AI
AI should be equipped with the mechanism that
explains the reasoning path and what data is used to
reach the results
Whistle blower against peculiar/strange behavior of AI
Redress or rescue package is to be legitimized
Insurance is also needed
26
27. Implementation of AI Ethics
Transparency Explainability
Understandability
Accountability
Trust
27
28. Single AI system is too complex and
being black box XAI
• XAI became a big research topic in recent years, such as XAI2017,
XAI2018
– The methods to give meanings of internal variables with
the combination of input variables.
– It seems not to be working for Deep Learning ‘cause of its
high dimensionality and complexity.
– Explanation is generated not via AI itself but via a simple
simulator such as decision list, decision tree, etc.
– As for the way to make output be understandable
explanation for ordinary people, promising results have
not yet come out.
28
29. Transparency and Accountability
• IEEE EADversion2 Law chapter says:
• We need to clarify who is responsible in case
of accidents
• For this Transparency and Accountability
29
30. Transparency
• Disclose the followings:
Learning data for ML and input data of actual use
of AI application generated by ML
Data flow and algorithm of AI application.
Conceptual data flow is OK
Investor, Founder and developer of AI application
system
30
31. Misunderstood version of
Accountability
• Wrong one
– Disclosing information via transparency with
natural language document for users of AI
application system
– In Japan, the mistranslation into “responsible to
explain 説明責任” is badly effecting many
people’s attitudes towards accountability (Prof.
Ohya: Keio Univ.)
31
32. Accountability must be recognized as:
• Explain the validity, fairness and legitimacy of
result/output of AI with the manner that AI
application users who are ordinary citizen can
easily understand and accept.
To clarify who are responsible for the results
of AI application outputs.
Responsibility implies compensation.
32
33. New Directions
Technically speaking, we have to think not only
about single AI but about group AI
They have to have the ability to generate easily
understandable explanations for ordinary
people tough !
Then how?
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34. The direction of utilizing AI:
recommendation
Towards TRUST
Trust: Making some one be authority based on
historical accumulation of technology
advancement
Licensing this authority by public authority such
as national government: i.e. medical doctor,
lawyer
Compensation for accidents: when responsive
persons are not clearly identified, insurance
comes to be the last resort.
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35. Trustworthy AI (EU)
• Lawful, Ethical, Robust
• Requirements
1. Human agency and oversight
2. Technical robustness and safety
3. Privacy and data governance
4. Transparency
5. Diversity non-discrimination and fairness
6. Societal and environmental well-being
7. accountability
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36. Single AI Drone used as a weapon
• AI drones are operated from a remote
operating center , even thousands of Km
– Complexity of battle field
– Responsible person could be unclear because of
latency time, difficulties of recognizing the real
enemy.
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37. Single AI Drone used as a weapon
– It is tough to identify who are soldiers and who
are civilians.
– To solve this problem, every persons’ data might
be gathered for long period of time and analyzed
with big data mining technologies to identify who
are enimies.
– worse but anyway, accountability is recognized
as a key factor.
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39. Unpredictability of Group AI’s behavior
• Platoon of autonomous AI drones
– If an attack comes out unintentionally where
human commanders are set aside, it is unclear
who is responsible Unintentionally happening
of battle, even war!
– No accountability is a problem!
– CCW(Convention on Certain Conventional
Weapons) tries to ban it, as far as I know
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40. Autonomous AI weapon
AI’s action liability immunity Strict
liability
Unjustified
damage
Autonomous
AI weapon
Unjustified
acts (mis
attack)
AI weapon
developer +
commander
Political
decision
AI weapon
developer
(wrong
design of
attack
checking)
International
laws
AI weapon as
a controllable
tool
operator
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41. Unpredictability of group AIs
Flush crash
• Flush crash: Group of AI traders communicate
each other via i.e. stock prices as common
language, and catastrophic results comes out in
seconds
– Deals in micro seconds
– Companies do not disclose AI traders’ algorithm
because of enterprise secret policies
No accountability!
42. How to cope with
• AI traders’ algorithms are still in secret
• Observing the market from outside by another special
AI: AI observer
• AI observers try to find unusual situation as early as
possible: Unusual situation detection technologies:
good research topic of AI
– Detected then stop
– Before detection, the loss or gain are exemption of liability
– The problem is when the system stops.
The problem caused by AI should be solved by AI
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43. AI observer observes the behavior of group of AI
and tries to
detect unusual situation as early as possible.
AI
AI observer
We should make a scheme
on which we trust this AI
Observer!
44. Conclusion
• Combination of Transparency, Accountability
including AI observers, Licensing, and
Compensation by insurance makes AI system
based on machine learning technologies be
trusted by every people including ordinary
citizens.
• This is good for us ML and AI researchers and
developers.
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