This document discusses a presentation on demystifying artificial intelligence and solving difficult problems. The presentation covers topics such as why AI experiences can be challenging, what AI is, different types of machine learning, how humans teach and monitor AI systems, ensuring AI is designed responsibly, and communicating about AI systems. It uses examples such as a hypothetical lawn care treatment selection system to illustrate concepts around data collection and training, potential biases, and unintended consequences that can arise.
Designing AI for Humanity at dmi:Design Leadership Conference in BostonCarol Smith
As design leaders we must enable our teams with skills and knowledge to take on the new and exciting opportunities that building powerful AI systems bring. Dynamic systems require transparency regarding data provenance, bias, training methods, and more, to gain user’s trust. Carol will cover these topics and challenge us as design leaders, to represent our fellow humans by provoking conversations regarding critical ethical and safety needs.
Presented at dmi:Design Leadership Conference in Boston in October 2018.
UX in the Age of AI: Leading with Design UXPA2018Carol Smith
How can designers improve trust of cognitive systems? What can we do to make these systems transparent? What information needs to be transparent? The biggest challenges inherent with AI will be discussed, specifically the ethical conflicts and the implications for your work, along with the basics of these concepts so that you can strive for making great AI systems.
Dynamic UXR: Ethical Responsibilities and AI. Carol Smith at Strive in TorontoCarol Smith
Artificially intelligent (AI) technologies are exciting and with them come a lot of new user experience research (UXR) responsibilities. How do we understand and clarify our users need for transparency, control, and access (and more) when the system is constantly changing?
These dynamic systems are already part of our everyday lives and quickly becoming part of our jobs. What are our responsibilities with regard to ethics and protecting users from bias?
Presented at Strive, June 7, 2019 in Toronto, Ontario, Canada. Strive is the 2019 UX Research Conference presented by the UX Research Collective Inc.
Designing Trustable AI Experiences at World Usability Day in ClevelandCarol Smith
How can designers improve trust of cognitive systems? What can we do to make these systems transparent? What information needs to be transparent? The biggest challenges inherent with AI will be discussed, specifically the ethical conflicts and the implications for your work, along with the basics of these concepts so that you can distinguish between simply smart systems and AI.
Presented at the World World Usability Day 2018 celebration in Cleveland, Ohio.
IA in the Age of AI: Embracing Abstraction and Change at IA Summit 2018Carol Smith
This session focuses on the questions we need to ask to create good, ethical experiences for our users.
Information Architects must push to…
- Keep people at the center of our work.
- Lead with our user’s goals.
- Ease of use, usability, findability, effectiveness, efficiency…
We must work to mature organizations approach
- Push back on “technology first” ideas.
- Lead on ethics - for our users, humanity.
Prototyping for Beginners - Pittsburgh Inclusive Innovation Summit 2019Carol Smith
To design for inclusion we often must try out different ideas. In this interactive session you'll learn about all types of prototyping and how to get feedback on your ideas from your users. This session will briefly introduce a variety of prototypes and materials and evaluation methods for early learning.
Participants will have time to build a quick prototype and practice getting feedback on it. We'll cover designing for accessibility and inclusion even at the prototype stage. You'll have the information you need to launch your ideas as early as possible to learn from the experience and improve more quickly.
Presented at the Pittsburgh Inclusive Innovation Summit March 30, 2019 held at Point Park University.
IA is Elemental: People are Fundamental at World IA Day 2020 PittsburghCarol Smith
Information architects work in a system with ourselves at the center. We are fundamental to making great experiences and as such, we must care for ourselves in order to best represent the people using the systems we are creating. Prioritizing the needs of users comes next, and with that protecting them by caring about diversity, inclusion and ethics. Finally, collaboration with colleagues and communities that influence our work can be done by educating them about IA work.
Designing AI for Humanity at dmi:Design Leadership Conference in BostonCarol Smith
As design leaders we must enable our teams with skills and knowledge to take on the new and exciting opportunities that building powerful AI systems bring. Dynamic systems require transparency regarding data provenance, bias, training methods, and more, to gain user’s trust. Carol will cover these topics and challenge us as design leaders, to represent our fellow humans by provoking conversations regarding critical ethical and safety needs.
Presented at dmi:Design Leadership Conference in Boston in October 2018.
UX in the Age of AI: Leading with Design UXPA2018Carol Smith
How can designers improve trust of cognitive systems? What can we do to make these systems transparent? What information needs to be transparent? The biggest challenges inherent with AI will be discussed, specifically the ethical conflicts and the implications for your work, along with the basics of these concepts so that you can strive for making great AI systems.
Dynamic UXR: Ethical Responsibilities and AI. Carol Smith at Strive in TorontoCarol Smith
Artificially intelligent (AI) technologies are exciting and with them come a lot of new user experience research (UXR) responsibilities. How do we understand and clarify our users need for transparency, control, and access (and more) when the system is constantly changing?
These dynamic systems are already part of our everyday lives and quickly becoming part of our jobs. What are our responsibilities with regard to ethics and protecting users from bias?
Presented at Strive, June 7, 2019 in Toronto, Ontario, Canada. Strive is the 2019 UX Research Conference presented by the UX Research Collective Inc.
Designing Trustable AI Experiences at World Usability Day in ClevelandCarol Smith
How can designers improve trust of cognitive systems? What can we do to make these systems transparent? What information needs to be transparent? The biggest challenges inherent with AI will be discussed, specifically the ethical conflicts and the implications for your work, along with the basics of these concepts so that you can distinguish between simply smart systems and AI.
Presented at the World World Usability Day 2018 celebration in Cleveland, Ohio.
IA in the Age of AI: Embracing Abstraction and Change at IA Summit 2018Carol Smith
This session focuses on the questions we need to ask to create good, ethical experiences for our users.
Information Architects must push to…
- Keep people at the center of our work.
- Lead with our user’s goals.
- Ease of use, usability, findability, effectiveness, efficiency…
We must work to mature organizations approach
- Push back on “technology first” ideas.
- Lead on ethics - for our users, humanity.
Prototyping for Beginners - Pittsburgh Inclusive Innovation Summit 2019Carol Smith
To design for inclusion we often must try out different ideas. In this interactive session you'll learn about all types of prototyping and how to get feedback on your ideas from your users. This session will briefly introduce a variety of prototypes and materials and evaluation methods for early learning.
Participants will have time to build a quick prototype and practice getting feedback on it. We'll cover designing for accessibility and inclusion even at the prototype stage. You'll have the information you need to launch your ideas as early as possible to learn from the experience and improve more quickly.
Presented at the Pittsburgh Inclusive Innovation Summit March 30, 2019 held at Point Park University.
IA is Elemental: People are Fundamental at World IA Day 2020 PittsburghCarol Smith
Information architects work in a system with ourselves at the center. We are fundamental to making great experiences and as such, we must care for ourselves in order to best represent the people using the systems we are creating. Prioritizing the needs of users comes next, and with that protecting them by caring about diversity, inclusion and ethics. Finally, collaboration with colleagues and communities that influence our work can be done by educating them about IA work.
Designing Trustworthy AI: A User Experience Framework at RSA 2020Carol Smith
Artificial intelligence (AI) holds great promise to empower us with knowledge and scaled effectiveness. To harness the power of AI systems, we can—and must—ensure that we keep humans safe and in control. This session will introduce a new user experience (UX) framework to guide the creation of AI systems that are accountable, de-risked, respectful, secure, honest and usable.
Presented at the RSA Conference 2020 in San Francisco, CA on February 28, 2020.
Design vs.Cancer: Patients Win UXDC 2017Carol Smith
This 10 minute talk concluded with a panel with two other presenters.
How do you make an impact on people’s lives in three weeks? I was selected to work on a pro-bono project to help health providers in Ethiopia, Nigeria and Uganda to increase the availability and lower the cost of cancer treatments. We were challenged to create a fully functioning software solution that would meet the needs of health care workers in forecasting the need for chemotherapy during the 3-week timeline. I’ll share the experience of working on a fast-paced project with a cross-functional team in this session.
We worked closely with the American Cancer Society, Clinton Health Access Initiative, NCCN and oncologists to create the ChemoQuant solution.
Artificial Intelligence & Software Testing: Hype or Hysteria?Johan Steyn
The slides from my talk at SIGiST Johannesburg on 20 June 2018. The video will be available in a few days - sign up at www.thebusinessoftesting.com
The URL of the video on the last slide: https://youtu.be/Y9FOyoS3Fag
Intelligence Augmentation - The Next-Gen AIMelanie Cook
Robotics and AI have integrated human and mechanical capabilities at work, with jobs lost and skills condensed to a keystroke. But human intelligence is far from obsolete.
With crowd-computing we have knowledge exchanges like Wiki, and real-time curated news. Semantic technology helps leaders to understand what is happening in the work place. But neurology shows that these leaders cannot make choices, and therefore take action, without emotion.
Augmented Intelligence takes human intuition and imagination, and combines it with AI’s ability to automate and scale, making the Intelligent Workplace hard to beat.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
Applying Machine Learning and Artificial Intelligence to BusinessRussell Miles
Machine Learning is coming out of the halls of Academia and straight into the arms of those businesses looking for a competitive edge.
This session by the experts of GoDataScience.io on machine learning is designed to give a high level overview of the field of machine learning for business consumers covering:
- What Machine Learning is
- Where it came from
- Why we need it
- Why now
- How to make it real with the various toolkits and processes.
Quantitative Ethics - Governance and ethics of AI decisionsNikita Lukianets
Presented as a part of the conference "Robots and Artificial Intelligence: The new force awakens" held in Nice, France in March 2018. This presentation provides framework and strategies to approach ethical aspects in the development of the AI of tomorrow.
The main topics discussed:
1) Data is the new electricity
2) Artificial intelligence and the decision making
3) Ethical frameworks for artificial intelligence
Business growth principles in the new economy Ashish Bedekar
A presentation I made @ Palava #Smartcity #startup accelerator on 19th April 2018. http://bit.ly/2qz5Xr2
#1 About me:
You may like to check out https://ashishbedekar.fyi.to/ecosystems which gives an overview of my profile, including LinkedIn recommendations
#2: Connect with me http://www.linkedin.com/in/ashishrbedekar | Twitter: @ashishrbedekar
#3 I believe in giving back to the community e.g
- Pro-bono startup advisor @Zone startup- an Indo-Canadian start-up Accelerator http://bit.ly/2o9XNqa
- Pro-bono startup advisor@ Supercharger Fintech accelerator ( KL, HK) http://bit.ly/2ErcM6S
- Pro-bono startup advisor@ NIT Trichy- International biz competition- http://bit.ly/2AQxqYu
-Mentor of change- Govt. of India- Atal innovation mission http://bit.ly/2HMdWrV
-Member of IET- IoT India (The IET is one of the world's largest multi-discipline professional societies of engineers with more than 160,000 members in 127 countries) http://bit.ly/2o9Pue9
-Mentor for startup boot camp-E- Cell- IIT Madras- one of India’s leading engineering college- http://bit.ly/2G6nRMg
Leaders across the world are looking out for different strategies thru which they can leverage AI.
Realizing this we have successfully organized an event on "AI 4 Institution Leaders" at Nasik focused on the need for AI for educational institutions for the first time in India.
A Theory of Knowledge Lecture given by Mark Steed, Director of JESS Dubai on Monday 4th March 2019
The lecture explains how AI works and then looks at some of the ethical implications
Best Practices for Scaling Data Science Across the OrganizationChasity Gibson
Effective data science in the enterprise is about aligning the right model, data, and infrastructure with the right outcomes. Most organizations today struggle to unlock the potential of data science to enhance decision-making and drive business value.
Join Forrester and Anaconda for a webinar to learn best practices for scaling data science across your entire organization. Guest speaker Kjell Carlsson, a Forrester Senior Analyst, and Peter Wang, Anaconda CTO, will share their unique perspectives on how to tackle five key challenges facing organizations today:
- Identifying, defining, and prioritizing valuable problems
- Building the right teams
- Leveraging the proper tools and platforms
- Iterating and deploying effectively
- Reaching end-users to generate value
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017Carol Smith
What is machine learning? Is UX relevant in the age of artificial intelligence (AI)? How can I take advantage of cognitive computing? Get answers to these questions and learn about the implications for your work in this session. Carol will help you understand at a basic level how these systems are built and what is required to get insights from them. Carol will present examples of how machine learning is already being used and explore the ethical challenges inherent in creating AI. You will walk away with an awareness of the weaknesses of AI and the knowledge of how these systems work.
[DSC Europe 22] On the Aspects of Artificial Intelligence and Robotic Autonom...DataScienceConferenc1
Autonomy in targeting is a function that could be applied to any intelligent system, in particular the rapidly expanding array of robotic systems, in the air, on land and at sea – including swarms of small robots. This is an area of significant investment and emphasis for many armed forces, and the question is not so much whether we will see more intelligent robots, but whether and by what means they will remain under human control. Today’s remote-controlled weapons could become tomorrow’s autonomous weapons with just a software upgrade. The central element of any future autonomous weapon system will be the software. Military powers are investing in AI for a wide range of applications10 and significant efforts are already underway to harness developments in image, facial and behavior recognition using AI and machine learning techniques for intelligence gathering and “automatic target recognition” to identify people, objects or patterns. Although not all autonomous weapon systems incorporate AI and machine learning, this software could form the basis of future autonomous weapon systems.
Designing Trustworthy AI: A User Experience Framework at RSA 2020Carol Smith
Artificial intelligence (AI) holds great promise to empower us with knowledge and scaled effectiveness. To harness the power of AI systems, we can—and must—ensure that we keep humans safe and in control. This session will introduce a new user experience (UX) framework to guide the creation of AI systems that are accountable, de-risked, respectful, secure, honest and usable.
Presented at the RSA Conference 2020 in San Francisco, CA on February 28, 2020.
Design vs.Cancer: Patients Win UXDC 2017Carol Smith
This 10 minute talk concluded with a panel with two other presenters.
How do you make an impact on people’s lives in three weeks? I was selected to work on a pro-bono project to help health providers in Ethiopia, Nigeria and Uganda to increase the availability and lower the cost of cancer treatments. We were challenged to create a fully functioning software solution that would meet the needs of health care workers in forecasting the need for chemotherapy during the 3-week timeline. I’ll share the experience of working on a fast-paced project with a cross-functional team in this session.
We worked closely with the American Cancer Society, Clinton Health Access Initiative, NCCN and oncologists to create the ChemoQuant solution.
Artificial Intelligence & Software Testing: Hype or Hysteria?Johan Steyn
The slides from my talk at SIGiST Johannesburg on 20 June 2018. The video will be available in a few days - sign up at www.thebusinessoftesting.com
The URL of the video on the last slide: https://youtu.be/Y9FOyoS3Fag
Intelligence Augmentation - The Next-Gen AIMelanie Cook
Robotics and AI have integrated human and mechanical capabilities at work, with jobs lost and skills condensed to a keystroke. But human intelligence is far from obsolete.
With crowd-computing we have knowledge exchanges like Wiki, and real-time curated news. Semantic technology helps leaders to understand what is happening in the work place. But neurology shows that these leaders cannot make choices, and therefore take action, without emotion.
Augmented Intelligence takes human intuition and imagination, and combines it with AI’s ability to automate and scale, making the Intelligent Workplace hard to beat.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
Applying Machine Learning and Artificial Intelligence to BusinessRussell Miles
Machine Learning is coming out of the halls of Academia and straight into the arms of those businesses looking for a competitive edge.
This session by the experts of GoDataScience.io on machine learning is designed to give a high level overview of the field of machine learning for business consumers covering:
- What Machine Learning is
- Where it came from
- Why we need it
- Why now
- How to make it real with the various toolkits and processes.
Quantitative Ethics - Governance and ethics of AI decisionsNikita Lukianets
Presented as a part of the conference "Robots and Artificial Intelligence: The new force awakens" held in Nice, France in March 2018. This presentation provides framework and strategies to approach ethical aspects in the development of the AI of tomorrow.
The main topics discussed:
1) Data is the new electricity
2) Artificial intelligence and the decision making
3) Ethical frameworks for artificial intelligence
Business growth principles in the new economy Ashish Bedekar
A presentation I made @ Palava #Smartcity #startup accelerator on 19th April 2018. http://bit.ly/2qz5Xr2
#1 About me:
You may like to check out https://ashishbedekar.fyi.to/ecosystems which gives an overview of my profile, including LinkedIn recommendations
#2: Connect with me http://www.linkedin.com/in/ashishrbedekar | Twitter: @ashishrbedekar
#3 I believe in giving back to the community e.g
- Pro-bono startup advisor @Zone startup- an Indo-Canadian start-up Accelerator http://bit.ly/2o9XNqa
- Pro-bono startup advisor@ Supercharger Fintech accelerator ( KL, HK) http://bit.ly/2ErcM6S
- Pro-bono startup advisor@ NIT Trichy- International biz competition- http://bit.ly/2AQxqYu
-Mentor of change- Govt. of India- Atal innovation mission http://bit.ly/2HMdWrV
-Member of IET- IoT India (The IET is one of the world's largest multi-discipline professional societies of engineers with more than 160,000 members in 127 countries) http://bit.ly/2o9Pue9
-Mentor for startup boot camp-E- Cell- IIT Madras- one of India’s leading engineering college- http://bit.ly/2G6nRMg
Leaders across the world are looking out for different strategies thru which they can leverage AI.
Realizing this we have successfully organized an event on "AI 4 Institution Leaders" at Nasik focused on the need for AI for educational institutions for the first time in India.
A Theory of Knowledge Lecture given by Mark Steed, Director of JESS Dubai on Monday 4th March 2019
The lecture explains how AI works and then looks at some of the ethical implications
Best Practices for Scaling Data Science Across the OrganizationChasity Gibson
Effective data science in the enterprise is about aligning the right model, data, and infrastructure with the right outcomes. Most organizations today struggle to unlock the potential of data science to enhance decision-making and drive business value.
Join Forrester and Anaconda for a webinar to learn best practices for scaling data science across your entire organization. Guest speaker Kjell Carlsson, a Forrester Senior Analyst, and Peter Wang, Anaconda CTO, will share their unique perspectives on how to tackle five key challenges facing organizations today:
- Identifying, defining, and prioritizing valuable problems
- Building the right teams
- Leveraging the proper tools and platforms
- Iterating and deploying effectively
- Reaching end-users to generate value
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017Carol Smith
What is machine learning? Is UX relevant in the age of artificial intelligence (AI)? How can I take advantage of cognitive computing? Get answers to these questions and learn about the implications for your work in this session. Carol will help you understand at a basic level how these systems are built and what is required to get insights from them. Carol will present examples of how machine learning is already being used and explore the ethical challenges inherent in creating AI. You will walk away with an awareness of the weaknesses of AI and the knowledge of how these systems work.
[DSC Europe 22] On the Aspects of Artificial Intelligence and Robotic Autonom...DataScienceConferenc1
Autonomy in targeting is a function that could be applied to any intelligent system, in particular the rapidly expanding array of robotic systems, in the air, on land and at sea – including swarms of small robots. This is an area of significant investment and emphasis for many armed forces, and the question is not so much whether we will see more intelligent robots, but whether and by what means they will remain under human control. Today’s remote-controlled weapons could become tomorrow’s autonomous weapons with just a software upgrade. The central element of any future autonomous weapon system will be the software. Military powers are investing in AI for a wide range of applications10 and significant efforts are already underway to harness developments in image, facial and behavior recognition using AI and machine learning techniques for intelligence gathering and “automatic target recognition” to identify people, objects or patterns. Although not all autonomous weapon systems incorporate AI and machine learning, this software could form the basis of future autonomous weapon systems.
AI and ML for Product Management by Smartsheet Sr Dir of PMProduct School
Product Management Event at #ProductCon Seattle on AI and ML for Product Management by Nitin Bhat, Senior Director of Product Management at Smartsheet.
Future of data science as a professionJose Quesada
How can you thrive in a future where machine learning has been popular for a few years already?
In this talk, I will give you actionable advice from my experience training serious data scientists at our retreat center in Berlin. You are going to face these pointy, hard questions:
- What is the promise of machine learning? Has it happened yet?
- Is it easy to take advance of machine learning, now that most algorithms are nicely packaged in APIs and libraries?
- How much time should I spend getting good at machine learning? Am I good enough now?
- Are data scientists going to be replaced by algorithms? Are we all?
- Is it easy to hire talent in machine learning after the explosion of MOOCs?
Algorithm Marketplace and the new "Algorithm Economy"Diego Oppenheimer
Talk by Diego Oppenheimer CEO of Algorithmia.com at Data Day Texas 2016.
Peter Sondergaard VP of Research for Gartner recently said the next digital gold rush is "How we do something with data not just what you do with it". During this talk we will cover a brief history of the different algorithmic advances in computer vision, natural language processing, machine learning and general AI and how they are being applied to Big Data today. From there we will talk about how algorithms are playing a crucial part in the next Big Data revolution, new opportunities that are opening up for startups and large companies alike as well as a first look into the role Algorithm Marketplaces will play in this space.
Reinforcement Learning In AI Powerpoint Presentation Slide Templates Complete...SlideTeam
Showcase how machines are built to perform intelligent tasks by using our content-ready Reinforcement Learning In AI PowerPoint Presentation Slide Templates Complete Deck. Take advantage of these artificial intelligence PowerPoint visuals, and describe how machine learning models are trained to make sequences of decisions in a complex environment. Showcase the types of artificial intelligence such as deep learning, machine learning. Explain the concept of machine learning which delivers predictive models based on the data fed into machine learning algorithms. Take the assistance of our visually attention-grabbing reinforcement learning PowerPoint templates and discuss the effective uses of artificial intelligence in various areas such as supply chain, human resources, fraud detection, knowledge creation, research, and development, etc. You can also present the usage of AI in healthcare. This includes treatment, diagnosis, training and research, early detection, etc. Explain the working of machine learning by downloading our attention-grabbing supervised learning PowerPoint presentation. https://bit.ly/3kQBnEZ
Presenter: Mukund Seshadri
A very high level brief overview of the different types of Machine Learning strategies and what Product Managers need to know about incorporating ML into their Products.
"A software engineer turned Technical Product Manager. I work at Schneider Electric helping ensure Life is On across the world.
Life is too short to build products that people don't want."
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/
We have critically evaluated how AI will shape integration use cases, their feasibility, and timelines. Emerging Technology Analysis Canvas (ETAC), a framework built to analyze emerging technologies, is the methodology of our study.
We observe that AI can significantly impact integration use cases and identify 13 AI-based use case classes for integration. Points to note include:
Enabling AI in an enterprise involves collecting, cleaning up, and creating a single representation of data as well as enforcing decisions and exposing data outside, each of which leads to many integration use cases. Hence, AI indirectly creates demand for integration.
AI needs data, which in some cases lead to significant competitive advantages. The need to collect data would drive vendors to offer most AI products in the cloud through APIs.
Due to lack of expertise and data, custom AI model building will be limited to large organizations. It is hard for small and medium size organization to build and maintain custom models.
An Elementary Introduction to Artificial Intelligence, Data Science and Machi...Dozie Agbo
This presentation is a friendly introduction to Artificial Intelligence, Data Science and Machine Learning. It touches on the beginnings of AI, the steps involved in Data Science, the roles involving operations on data, and the buzz around "Technology Singularity".
It ends by looking at tools and system requirements for people who might want to start a career in AI.
Have fun exploring Artificial Intelligence!
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...Analytics India Magazine
Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
Similar to Demystifying Artificial Intelligence: Solving Difficult Problems at ProductCamp Pittsburgh (20)
Navigating the Complexity of Trust at UXPA Boston 2021Carol Smith
Trust is complex and transient. Context, safety, privacy, respect, and many other considerations are built into each individuals’ concept of trust. How can we examine this complexity in a way that supports the work of making digital experiences? What research supports this work and how can we use practices of responsible development to make systems that earn appropriate levels of trust? What is an appropriate level of trust for emerging technologies such as machine learning systems? This talk will examine trust and how UX practitioners can define and measure it.
Carol J. Smith
September 24, 2021
Carnegie Mellon University, SEI
Twitter: @carologic @sei_etc
Implementing Ethics: Developing Trustworthy AI PyCon 2020Carol Smith
Ethics discussions abound, but translating “do no harm” into our work is frustrating at best, and obfuscatory at worst. We can agree that keeping humans safe and in control is important, but implementing ethics is intimidating work.
Learn how to wield your preferred technology ethics code to make an AI system that is accountable, de-risked, respectful, secure, honest and usable. The presenter will introduce the topic of ethics and then step through a user experience (UX) framework to guide AI development teams successfully through this process.
Presented virtually for PyCon 2020 which was to be held in Pittsburgh, PA, but was reorganized online due to Covid-19.
Gearing up for Ethnography, Michigan State, World Usability Day 2019Carol Smith
Prepping for UX research can be intimidating, and there is never enough time or resources. Carol will share her personal experiences in the field, both good and bad. She has learned the hard way, doing observations in moving vehicles, coal mines, hospitals, schools, homes, and offices. She will also share interesting anecdotes from colleagues and review both ethical and behavioral standards for researchers. The key is to prepare well, learn to be flexible and to adapt to the situation.
Presented at World Usability Day 2019 at Michigan State University with Michigan UXPA
Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Developm...Carol Smith
"Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Development" is a paper presented at the AAAI 2019 Fall Symposium on AI in Government and the Public Sector, (sponsored by the Association for the Advancement of Artificial Intelligence) in Washington, DC, November 7–9, 2019.
Artificial intelligence (AI) holds great promise to empower us with knowledge and augment our effectiveness. We can -- and must -- ensure that we keep humans safe and in control, particularly with regard to government and public sector applications that affect broad populations. How can AI development teams harness the power of AI systems and design them to be valuable to humans? Diverse teams are needed to build trustworthy artificial intelligent systems, and those teams need to coalesce around a shared set of ethics. There are many discussions in the AI field about ethics and trust, but there are few frameworks available for people to use as guidance when creating these systems. The Human-Machine Teaming (HMT) Framework for Designing Ethical AI Experiences described in this paper, when used with a set of technical ethics, will guide AI development teams to create AI systems that are accountable, de-risked, respectful, secure, honest, and usable. To support the team's efforts, activities to understand people's needs and concerns will be introduced along with the themes to support the team's efforts. For example, usability testing can help determine if the audience understands how the AI system works and complies with the HMT Framework. The HMT Framework is based on reviews of existing ethical codes and best practices in human-computer interaction and software development. Human-machine teams are strongest when human users can trust AI systems to behave as expected, safely, securely, and understandably. Using the HMT Framework to design trustworthy AI systems will provide support to teams in identifying potential issues ahead of time and making great experiences for humans.
On the Road: Best Practices for Autonomous Experiences at WUC19Carol Smith
Presented at the World Usability Congress in Graz, Austria on October 16, 2019.
Self-driving vehicles are still a rarity in most cities, but as they become more common and as more and more humans interact with them we need to consider the wide variety of human experiences that occur within and along-side these vehicles. What information does the driver need when the vehicle is getting started vs. on it’s way? What information engenders trust and how much is too much? What changes due to experience level and comfort? How do we account for reliable easy commutes and people who use vehicles differently each day? How do these vehicles interact with other drivers, pedestrians, bicyclists and general society?
Designing More Ethical and Unbiased Experiences - AbstractionsCarol Smith
Presented at Abstractions, Pittsburgh, PA
Karen Bachmann and Carol Smith, August 23, 2019
Humans are biased, and sadly, we are not always able to filter our deeply ingrained biases. UX designers and researchers have long understood this, but as we watch major technology companies make significant mistakes with regard to ethics and bias, the cost of not accounting for bias and ethics is becoming more evident and widely known.
Even knowing what pitfalls exist, we still miss opportunities for doing good as a result of our own human biases obscuring our vision. We need tools to explore and challenge our biases in a productive way to deliver better outcomes. We need a set of shared values within teams and, ultimately, across the industry to promote our common responsibility to deliver the greatest benefit while causing the least amount of harm. How can we work together to intensify the focus on ethical design? In this session, we’ll share ways you can empower yourself and your teams to do the right thing for people.
Navigating challenges in IA people management at IAC19Carol Smith
Whether you are building a team, managing experience practitioners or navigating career changers, managing a team of creative and analytical IA practitioners can be challenging. The welcome change towards diverse and inclusive hiring practices can add even more challenges.
Learn how an experienced manager navigated through painful challenges and wonderful successes while managing large and small design departments in organizations with employees around the world. Presented at IA Conference 2019 in Orlando Florida by Carol Smith.
What can DesignOps do for you? by Carol Smith at TLMUX in MontrealCarol Smith
You have probably seen the terms DesignOps and/or ResearchOps float by in your social media queue. These teams make designing (and researching) at scale beautifully efficient and successful. Carol steps through how these teams work, the types of activities they perform, situations they are helpful for, and ways you can leverage these types of programs in your organization. Carol will share examples from her experiences and stories from other organizations that are using Design Ops to do effective design at scale.
Presented at Tout le monde UX in Montreal, Quebec, Canada on February 28, 2019. http://toutlemonde-ux.com/
Gearing up for Ethnography at Midwest UX 2018Carol Smith
We are all low on time and resources, and our UX research must occur wherever and whenever possible. Carol will share her personal experiences in the field, both good and bad. She has learned the hard way doing observations in moving vehicles, coal mines, hospitals, schools, homes, and offices. She will also share interesting anecdotes from colleagues and review both ethical and behavioral standards for researchers. The key is to prepare well, learn to be flexible and to adapt to the situation.
Presented at Midwest UX 2018 held in Chicago, IL.
Product Design in Agile Environments: Making it Work at ProductCamp PittsburghCarol Smith
Can Product Design work in Agile environments? Yes! Balancing people and process can be complicated, and in this talk, Carol will provide you guidance to make it work. You can inform good design with strong user experience (UX) research and support continuous releases in a fast-paced environment. We'll look at ways to achieve a flexible approach that meets the needs of these seemingly conflicting efforts. Participants will come away with the tools they need to successfully integrate design thinking methods, in an Agile environment, one sprint at a time.
Selected for presentation at ProductCamp Pittsburgh in September 2018 at Carnegie Mellon University (CMU).
Making Great User Experiences, Pittsburgh Scrum MeetUp, Oct 17, 2017Carol Smith
Everything is designed, yet some interactions are much better than others. What does it take to make a great experience? What are the areas that UX specialists focus on? How do skills in cognitive psycology, computer science and design come together? Carol introduces basic concepts in user experience design that you can use to improve the user's expeirence and/or clearly communicate with designers.
UX in the Age of AI: Where Does Design Fit In? Fluxible 2017Carol Smith
Cognitive computing and machine learning are not new concepts, but they are new to most UX’ers. Carol Smith addresses questions about artificial intelligence (AI) such as:
- What are these terms and technologies and how do they work?
- How can we take advantage of these powerful systems to help our users?
- Should I be concerned that computers will take over the world soon? Spoiler: It is extremely unlikely.
Once this baseline understanding is established, we’ll look at examples of AI in use and discuss the relevancy of design work in the age of AI. Additionally, we’ll explore the ethical challenges inherent with the use of AI from the user’s perspective, specifically regarding trust and transparency.
This was presented at Fluxible 2017 in Kitchener-Waterloo, Ontario, Canada on 23 Sept 2017.
Making Great User Experiences at Cleveland C# .Net Meetup July 27 2017Carol Smith
Everything is designed, yet some interactions are much better than others. What does it take to make a great experience? Carol introduces basic concepts in user experience design that you can use to improve your work. You'll learn the basics of: cognitive psychology; accessibility; design thinking; interaction design; and visual design. These concepts work together to make great user experiences and Carol will help you to understand how this work can be integrated into your existing software development process.
Faster Usability Testing in an Agile World - Agile UX Virtual Summit 2017 by ...Carol Smith
Carol Smith presented "Faster Usability Testing in an Agile World" via webinar during the Agile UX Virtual Summit 2017 by UXPin.
This presentation covers:
- Brief intro to how the IBM Watson Design team runs continuous usability tests and integrates the UX team
- How design work fits into the Agile process via dual track development
- When to run moderated, un-moderated, remote, and in-person studies
- How to effectively communicate UX findings and recommendations
Making Faster UX in an Agile World - HOAPitt 2017Carol Smith
Carol Smith presented this topic at the Heart of Agile conference in Pittsburgh, PA in April 2017.
UX Slows Agile down! Do you hear that a lot? Carol shared best practices and how to dispel this myth in this session. The presentation included discussions of how to successfully embedding the UX team and the pros and cons of Agile projects. Carol will introduce methods for the UX team to break down and include their work in the backlog so it can get prioritized. Finally, Carol will discuss several successful ways to integrate usability testing across iterations.
AI for IA's: Machine Learning Demystified at IA Summit 2017 - IAS17Carol Smith
What is machine learning? Is IA relevant in the age of AI? How can I take advantage of cognitive computing? Learn the basics of these concepts and the implications for your work in this presentation. Carol Smith provides examples of machine learning use and will discuss the challenges inherent in in AI.
DIY Usability Testing for Business Analysts (BA)Carol Smith
This presentation provides techniques for business analysts (BA's) to begin conducting their own usability tests. This was presented to the Pittsburgh IIBA Chapter on January 9, 2017.
Mature Products: The Cycle of UX Reinvention UXPA 2016Carol Smith
As products mature, the user’s needs change over time and so must the way we work. This presentation discusses various experiences working on mature software and complex Web applications and a set of best practices.
Presented to the IIBA (International Institute of Business Analysis) Pittsburgh, PA Chapter on February 8, 2016.
Most Business Analysts have plenty of experience when it comes to mapping database fields across a system interface, but where do you put those fields in a user interface? As BAs, we're used to wearing different hats: Project Manager, Tester, Developer. But since the invention of the iPod, everyone is becoming more aware of User Experience.
We've all experienced a hard-to-use website or those old green screen applications where you had the F-Key menus memorized by the end of your first month. But F-Keys and clicking through ten pages of options to get to the submit button won't cut it anymore.
In this presentation Carol Smith walks us through some basics to help you create a User Experience that won't make your end users to throw up their hands in frustration.
Methods such as user focused interviews, card sorts and usability testing best practices are introduced with the intent that BA's can use these tools immediately in their workplace.
Can AI do good? at 'offtheCanvas' India HCI preludeAlan Dix
Invited talk at 'offtheCanvas' IndiaHCI prelude, 29th June 2024.
https://www.alandix.com/academic/talks/offtheCanvas-IndiaHCI2024/
The world is being changed fundamentally by AI and we are constantly faced with newspaper headlines about its harmful effects. However, there is also the potential to both ameliorate theses harms and use the new abilities of AI to transform society for the good. Can you make the difference?
ARENA - Young adults in the workplace (Knight Moves).pdfKnight Moves
Presentations of Bavo Raeymaekers (Project lead youth unemployment at the City of Antwerp), Suzan Martens (Service designer at Knight Moves) and Adriaan De Keersmaeker (Community manager at Talk to C)
during the 'Arena • Young adults in the workplace' conference hosted by Knight Moves.
Visual Style and Aesthetics: Basics of Visual Design
Visual Design for Enterprise Applications
Range of Visual Styles.
Mobile Interfaces:
Challenges and Opportunities of Mobile Design
Approach to Mobile Design
Patterns
Maximize Your Content with Beautiful Assets : Content & Asset for Landing Page pmgdscunsri
Figma is a cloud-based design tool widely used by designers for prototyping, UI/UX design, and real-time collaboration. With features such as precision pen tools, grid system, and reusable components, Figma makes it easy for teams to work together on design projects. Its flexibility and accessibility make Figma a top choice in the digital age.
Fonts play a crucial role in both User Interface (UI) and User Experience (UX) design. They affect readability, accessibility, aesthetics, and overall user perception.
Demystifying Artificial Intelligence: Solving Difficult Problems at ProductCamp Pittsburgh
1. Demystifying Artificial Intelligence:
Solving Difficult Problems
Carol Smith @carologic
ProductCamp Pittsburgh @PGHPCAMP
September 22, 2018
This work is licensed under a Creative
Commons Attribution-NonCommercial
4.0 International License except where
noted otherwise.
9. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Making AI in Westworld
• Who made the data
– Host backstories and scripts
– Environmental design
• What is data’s provenance?
10. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Data creator: Writer
• Creates scripts and
stories for hosts/robots
• Determines how and
when will be presented
11. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Why should we care?
• What are his bias’?
– Straight, white male
– Reused scripts due to deadlines and a lack of creativity
– What else?
• How did this affect the experience?
• Does it matter?
13. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
System training and maintenance: Scientists
• Triage system when
there are issues
• Adjust programming
and settings
• Review previous
stories “Step into
analysis”
14. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Why should we care?
• What are their weaknesses’?
– Not very creative
– Seemingly minimal exposure to the rest of the world
16. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Programming
• Ford/Arnold as
programmer
• Westworld does
an excellent
job of explaining this
17. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Why should we care?
• No context - few mental models.
• Help understand what is going on.
19. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
To engender trust, provide transparency
• Data
• Training/programming of system
• Rationale/bias/logic
21. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
AI is dynamic
• Mature AI takes data and training
- applies to new situations.
• Attributions to new data may be:
– Inaccurate
– Weird
– Inappropriate
– Unintended
22. AI is present when computers/machines
– Exhibit intelligence
– Perceive their environment
– Take actions/make decision
to maximize chance of success at a goal
Our Road to Self-Driving Vehicles | Uber ATG
https://youtu.be/27OuOCeZmwI
23. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
AI/Cognitive computers are
• Made with algorithms.
• Limited domain knowledge – only what you teach.
• Control ONLY what we give them control of.
• Aware of nuances and can continue to learn.
24. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Number Five “Needs Input”
Short Circuit (1986 film)
Ally Sheedy and Number Five (Tim Blaney)
https://en.wikipedia.org/wiki/Short_Circuit_(1986_film)
25. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Types of AI – Machine Learning
• Supervised learning
– Input data and target variable
– Need specialist to do training
– Most common
Artificial Intelligence Demystified by. Rahul December 23, 2016. Analytics Vidhya
https://www.analyticsvidhya.com/blog/2016/12/artificial-intelligence-demystified/
26. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Annotating Content
Image created by Angela Swindell, Visual Designer, IBM
27. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Types of AI – Machine Learning
• Unsupervised learning
– Input data – machine defines patterns
• Reinforced learning
– Games – creator specifies rules and reward, machine learns
Artificial Intelligence Demystified by. Rahul December 23, 2016. Analytics Vidhya
https://www.analyticsvidhya.com/blog/2016/12/artificial-intelligence-demystified/
28. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Machine Training Creation Methods
Creation Method Duration Knowledge / Accuracy
Supervised
Programmed with knowledge transfer
Months - years Best
Supervised
Entry by content specialist
Weeks - months High potential
Unsupervised Days - weeks Core knowledge only
Reinforced learning Varies Highly accurate
30. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Pattern recognition
• Natural Language
Processing
• Image Analysis
• 88,000 retina images
– Recognize healthy eye
– Glaucoma #2 cause of
blindness worldwide
– 50% of cases undetected
IBM Watson https://twitter.com/IBMWatson/status/844545761740292096
31. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Deep Learning
• Best at classifying objects
based on features
• Can be applied
to other types of AI
• Lots of AI systems working
together
Toward ethical, transparent and fair AI/ML: a critical reading list, by Eirini Malliaraki, Feb 19 via
tweet from @robmccargow https://medium.com/@eirinimalliaraki/toward-ethical-transparent-and-
fair-ai-ml-a-critical-reading-list-d950e70a70ea
32. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Taxonomies and Ontologies Come to Life
(NOT like humans learn)
Photo: https://commons.wikimedia.org/wiki/File:Baby_Boy_Oliver.jpg
33. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Humans Teach and Monitor AI
• Water – add new information and teach (continuous)
• Thin – pluck poor performing models, bad patterns
• Prune – as AI matures, continuous monitoring,
adding and removing functionality.
• Cull – remove/stop broken/biased models.
34. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Use Case: Lawn care treatment selection
• Users: Lawn technicians and sales people.
• Goal: More quickly and effectively customize solutions
for customers and minimize costs
(time, effort, chemical amount, cost to customer, etc.).
35. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Hire a Consulting Firm
• Understand users, goals.
• Review existing data
– Knowledge about lawn care products.
– Great data from a few technicians.
– Create ground truth and teach AI (few weeks).
36. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Can’t Just Turn AI systems on
• Subject matter experts (SME’s) knowledge needed
– Lawyers
– Machinists
– Insurance adjusters
– Physicians
• Working with experts in AI systems.
37. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Lawn Care – Data and Training
• Data:
– Patterns across customers
– Effectiveness
of treatments.
– Extent of problems,
pests, etc.
• Training for:
– Types of grass.
– Conditions (sun
exposure, etc.).
– Customer attitude
about lawn care.
38. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Ready for Use
• Experts begin reviewing results
– Too many recommendations for heavy chemical use.
– Need to replace models that aren’t working.
• But what went wrong
40. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Use best practices
• Understand problem deeply
• Select AI system
– Different problems require different systems
• Build right AI system, in ethical way to solve problem
41. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Who will use the system and why?
• What are their goals?
• What problems are they trying to solve?
• Team/independent work?
42. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
How trusting are the users of AI?
• Work to gain trust?
• What might engender trust via the UI?
43. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
What questions most likely asked about data?
• What…
– Comes next?
– Outliers?
– Changed? How can I tell what changed?
– New? Unexpected?
– Validates assumptions?
– Increased/decreased frequency?
44. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Anticipated changes with AI system?
• We need an AI for that!
• Intention? Improvements?
• Better or faster?
• Scope?
45. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
What are potential unintended consequences?
• Understand user’s fears
• Become familiar
• Learn how to address
• Fears lead to potential unintended consequences
– Preparing for these will protect your users
46. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
AI Management Matches Org Ecosystem
• Microcosm of organization
– Not independent of organization - same issues
• Need similar support
– Curating content
– Watching for issues, etc.
– Managing, training, and oversight
48. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Data Source
• In existence?
• Available?
• High quantity?
• High quality?
Photo by sunlightfoundation
https://www.flickr.com/photos/sunlightfoundation/2385174105
49. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Curation
• Where content sourced from?
– Bias?
– Organization?
– Potential unintended consequences?
• Who is creating/curating collection?
– Respected experts in industry
– Diverse, socio-economic, cross cultural, international team
“3 guiding principles for ethical AI, from IBM CEO Ginni Rometty”
by Alison DeNisco. January 17, 2017, Tech Republic http://www.techrepublic.com/article/3-
guiding-principles-for-ethical-ai-from-ibm-ceo-ginni-rometty/
50. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
All data is biased - what is bias?
• All bring personal experience and knowledge
• Affected by:
– Social class, resource availability
– Race, Gender, Sexuality
– Culture, Theology, Tradition
– Other factors we aren’t even aware of
51. “We often have
no way of knowing
when and why people
are biased.”
- Sandra Wachter
Q&A: Should artificial intelligence be legally required to explain itself?
By Matthew Hutson, May. 31, 2017. Interview with Sandra Wachter, data ethics researcher at Univ. of Oxford and Alan Turing Institute.
http://www.sciencemag.org/news/2017/05/qa-should-artificial-intelligence-be-legally-required-explain-itself
52. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Lawn Care - what happened?
• Understand users, goals.
• Review existing data
– Knowledge about lawn care products.
– Great data from a few technicians.
– Create ground truth and teach AI (few weeks).
53. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Data Source Problematic
• Few technicians who are
great at documenting.
– Prefer using chemicals
to treat lawns.
– Limited data biased
towards chemical use.
• Most technicians, take
horrible notes.
• Prefer “all natural”
treatments.
Neither are wrong.
Limited data created a bias.
54. AI is only as good as data
and time spent improving it.
Biased based on what taught.
55. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Data created by those who write
• History written by victors
• Lawn care specialists
57. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Which experts will train the system?
• How are they vetted?
• How frequently will they be available?
• How will quality be maintained?
• Where will they work?
• What process will they use?
• When something goes wrong, how do you respond?
58. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
How important is accuracy?
• How accurate must the system be?
• Consider your colleagues
• You are creating another colleague…
59. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Across Industries – Priority of Accuracy Varies
Higher Priority
90-99%+
Lower Priority
60-89% accuracy is acceptable
Financial
Ecommerce
61. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Keep people at the center of our work
• Great solutions are made when they solve a problem
for people
• User’s goals
• Ethics
63. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Intentional Design
• Keep people and data safe
• When unintended consequences arise,
how do we deal with them?
64. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Make it your business to keep people safe
• Warning signs?
• How do we deal with unintentional consequences?
• What is the worst potential outcome?
65. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Make a Plan
• No need to imagine
every single situation
• Who is notified
immediately?
• What is the method for
“turning it off”?
• Unintended consequences
of turning it off?
Google’s new tensor processing units:
https://www.nytimes.com/2018/02/12/technology/google-artificial-intelligence-chips.html
66. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
What will you do?
• Focus on how you’ll react to worst situations:
• What happens when it becomes a Nazi?
• What happens when it does XYZ unexpectedly?
67. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Back doors and brakes
• A way to get into the system and shut it down
• Secured from inside and outside
• “If it’s not usable, it’s not secure.”
– Jared Spool, IAS17
Unintuitive and Insecure: Fixing the Failures of Authentication,
Jared Spool, IA Summit 2017
68. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Privacy and Ownership
• Who owns the data?
– User owns what and when?
– Organization owns what and when?
• What must a user reveal?
– Life expectancy of data?
– Transitional phases?
Ethical Issues in IS by Richard Mason’s
H/T to Andrea Resmini
https://www.gdrc.org/info-design/4-ethics.html
69. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Access to Data
• What do we have a right to access?
• Barriers
– Literacy and awareness.
– Connection to internet – economics and location.
– Access to pertinent data.
70. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Who gets to use our tools?
• “When we do not design for people with disabilities
we are being ableist”
– Anne Gibson @perpendicularme #ias18 Roundtable on Ethics
How People with Disabilities Use the Web: Overview https://www.w3.org/WAI/intro/people-use-web /
72. Grady Booch, Scientist, philosopher, IBM’er https://www.ted.com/talks/grady_booch_don_t_fear_superintelligence
Our Responsibility:
Ensure that humans
can unplug
the machines!
73. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Communicating
About
The System
Strong Bad Email #45 – Techno - Strong Bad makes a techno song.
https://youtu.be/JwZwkk7q25I Homestarrunnerdotcom Published on Mar 31, 2009
74. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Communicate Responsibly
• How is communication about the AI handled?
– How do you report issues?
• To whom?
– Everyone needs to be bought in.
– Not everyone can fix it.
75. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Potential Bias
• Show awareness; acknowledge issues.
– What are potential signs of building bias?
– Over communicate about potential bias.
• Acknowledge potential for bad decisions
based on data.
– Who is responsible?
76. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Data and Training Transparency
• What data was it based on?
– Sources referenced?
– Access to overall collection?
• Who trained AI?
– Experience? Background? Vetting?
– Who will update it? How often?
77. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
How do users know when something is wrong?
• Show examples of changes.
• Where do these examples live?
• How can a user contest something and report it?
78. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Confidence in Content
• Representing confidence.
• How is ‘ABC’ comparable to other entries?
79. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Displaying Information
• AI generated content
– separate from other content?
– marked as AI generated?
– more clearly referenced?
– does it matter?
84. Humans teach what we feel is important… teach them to share our values.
Grady Booch, Scientist, philosopher, IBM’er https://www.ted.com/talks/grady_booch_don_t_fear_superintelligence
86. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Create a code of conduct/ethics
• What do you value?
• What lines won’t your AI cross?
• How will you track your progress?
87. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
To engender trust, provide transparency
• Who made the data?
• Who has access to users’ data/useage?
• Who trained/programmed the system?
• Why system providing data it is?
88. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Take Responsibility
• Keep humans in control.
• Hire/work with people affected by bias
– Non-typical schools, non-typical careers, etc.
– POC, LGBTQ+, women
• Conduct auditing
How to Keep Your AI from Turning into a Racist Monster by Megan
Garciahttps://www.wired.com/2017/02/keep-ai-turning-racist-monster/
89. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Teach others about AI
• Demystify in plain language
• Teach others
• Provide easy way to raise concerns
(anonymous if appropriate)
90. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Great Problem Solvers…
• People and problems
• Not “technology first.”
• Ethics
91. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Learn about making ethical, transparent and fair AI
Toward ethical, transparent and fair AI/ML: a critical reading list, by Eirini Malliaraki, Feb 19 via tweet from
@robmccargow https://medium.com/@eirinimalliaraki/toward-ethical-transparent-and-fair-ai-ml-a-critical-
reading-list-d950e70a70ea
92. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Create Ethical AI
• Less-biased content
• Intentional design
• Communicate responsibly about AI
93. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Don’t fear AI - Explore AI
Try out tools (see Appendix and footer notes)
Pair with others
96. Does the Trolley Problem Have a Problem? What if your answer to an absurd hypothetical question had no bearing on how you behaved in real life?
By Daniel Engber. Slate.com. June 18, 2018. Image of anxious hypothetical trolley car lever operator by Lisa Larson-Walker
https://slate.com/technology/2018/06/psychologys-trolley-problem-might-have-a-problem.html
97. My AI is Alive!
It came up with something new
and we have no idea why or how!
100. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
AI Tools
• A list of artificial intelligence tools you can use today — for businesses, by Liam
Hanel, July 11, 2017 on Lyr.AI
https://lyr.ai/a-list-of-artificial-intelligence-tools-you-can-use-today%E2%80%8A-
%E2%80%8Afor-businesses/ and https://medium.com/imlyra/a-list-of-artificial-
intelligence-tools-you-can-use-today-for-personal-use-1-3-7f1b60b6c94f
• Best AI and machine learning tools for developers, By Christina Mercer, Sep 26,
2017 in Techworld from IDG https://www.techworld.com/picture-gallery/apps-
wearables/best-ai-machine-learning-tools-for-developers-3657996/
• 15 Top Open Source Artificial Intelligence Tools by Cynthia Harvey, September
12, 2016 on Datamation https://www.datamation.com/open-source/slideshows/15-
top-open-source-artificial-intelligence-tools.html
• IBM Watson Developer Tools (free trials):
https://console.ng.bluemix.net/catalog/?category=watson
101. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Want to Know More?
• The Rise Of Artificial Intelligence As A Service In The Public
Cloud
Rise Of Artificial Intelligence As A Service In The Public Cloud by Janakiram MSV , Forbes Article:
https://www.forbes.com/sites/janakirammsv/2018/02/22/the-rise-of-artificial-intelligence-as-a-service-in-the-public-cloud/#11aa85a8198e
Courses at http://www.fast.ai/
102. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
10 Major Milestones in the History of AI
https://www.analyticsvidhya.com/blog/2016/12/artificial-intelligence-demystified/
103. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Additional Resources
• “How IBM is Competing with Google in AI.” The Information. https://www.theinformation.com/how-ibm-is-
competing-with-google-in-ai?eu=2zIDMNYNjDp7KqL4YqAXXA
• “The business case for augmented intelligence” https://medium.com/cognitivebusiness/the-business-case-for-
augmented-intelligence-36afa64cd675
• “Comparison of machine learning methods applied to birdsong element classification” by David Nicholson.
Proceedings of the 15th Python in Science Conference (SCIPY 2016).
http://conference.scipy.org/proceedings/scipy2016/pdfs/david_nicholson.pdf
• “Staples’ “Easy Button” Comes to Life with IBM Watson” in Business Wire, October 25, 2016.
http://www.businesswire.com/news/home/20161025006273/en/Staples%E2%80%99-%E2%80%9CEasy-
Button%E2%80%9D-Life-IBM-Watson
• “How Staples Is Making Its Easy Button Even Easier With A.I.” by Chris Cancialosi, Forbes.
https://www.forbes.com/sites/chriscancialosi/2016/12/13/how-staples-is-making-its-easy-button-even-easier-
with-a-i/#4ae66e8359ef
• “Inside Intel: The Race for Faster Machine Learning”
http://www.intel.com/content/www/us/en/analytics/machine-learning/the-race-for-faster-machine-learning.html
104. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
More Resources
• “Update: Why this week’s man-versus-machine Go match doesn’t matter (and what does)” by Dana
Mackenzie. Science Magazine. Mar. 15, 2016 http://www.sciencemag.org/news/2016/03/update-why-week-s-
man-versus-machine-go-match-doesn-t-matter-and-what-does
• “For IBM’s CTO for Watson, not a lot of value in replicating the human mind in a computer.” by Frederic
Lardinois (@fredericl), TechCrunch, Posted Feb 27, 2017. https://techcrunch.com/2017/02/27/for-ibms-cto-
for-watson-not-a-lot-of-value-in-replicating-the-human-mind-in-a-computer/
• “Google and IBM: We Want Artificial Intelligence to Help You, Not Replace You” Most Powerful Women by
Michelle Toh. Mar 02, 2017. Fortune. http://fortune.com/2017/03/02/google-ibm-artificial-intelligence/
• “Facebook scales back AI flagship after chatbots hit 70% f-AI-lure rate - 'The limitations of automation‘” by
Andrew Orlowski. Feb 22, 2017. The Register https://www.theregister.co.uk/2017/02/22/facebook_ai_fail/
• “Microsoft is deleting its AI chatbot's incredibly racist tweets” by Rob Price. Mar. 24, 2016. Business Insider
UK. http://www.businessinsider.com/microsoft-deletes-racist-genocidal-tweets-from-ai-chatbot-tay-2016-3
Special Thanks: Soundtrack to 'Run Lola Run', 1998 German thriller film written and directed by Tom Tykwer,
and starring Franka Potente as Lola and Moritz Bleibtreu as Manni. Soundtrack by Tykwer, Johnny Klimek, and
Reinhold Heil
105. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Even More Resources
• “IBM’s Automated Radiologist Can Read Images and Medical Records” by Tom Simonite, February 4, 2016.
Intelligent Machines, MIT Technology Review. https://www.technologyreview.com/s/600706/ibms-automated-
radiologist-can-read-images-and-medical-records/
• “The IBM, Salesforce AI Mash-Up Could Be a Stroke of Genius” by Adam Lashinsky, Mar 07, 2017. Fortune.
http://fortune.com/2017/03/07/data-sheet-ibm-salesforce/
• "Google can now tell you're not a robot with just one click" by Andy Greenberg. Dec. 3, 2014. Security: Wired.
https://www.wired.com/2014/12/google-one-click-recaptcha/
• “Essentials of Machine Learning Algorithms (with Python and R Codes)” by Sunil Ray, August 10, 2015.
Analytics Vidhya. https://www.analyticsvidhya.com/blog/2015/08/common-machine-learning-algorithms/
• IBM on Machine Learning https://www.ibm.com/analytics/us/en/technology/machine-learning/
• “At Davos, IBM CEO Ginni Rometty Downplays Fears of a Robot Takeover” by Claire Zillman, Jan 18, 2017.
Fortune. http://fortune.com/2017/01/18/ibm-ceo-ginni-rometty-ai-davos/
• “Google and IBM: We Want Artificial Intelligence to Help You, Not Replace You” by Michelle Toh. Mar 02,
2017. Fortune. http://fortune.com/2017/03/02/google-ibm-artificial-intelligence/
106. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Yes, even more resources
• Video: “IBM Watson Knowledge Studio: Teach Watson about your unstructured data”
https://www.youtube.com/watch?v=caIdJjtvX1s&t=6s
• “The optimist’s guide to the robot apocalypse” by Sarah Kessler, @sarahfkessler. March 09, 2017. QZ.
https://qz.com/904285/the-optimists-guide-to-the-robot-apocalypse/
• “AI Influencers 2017: Top 30 people in AI you should follow on Twitter" by Trips Reddy @tripsy, Senior
Content Manager, IBM Watson . February 10, 2017 https://www.ibm.com/blogs/watson/2017/02/ai-
influencers-2017-top-25-people-ai-follow-twitter/
• “3 guiding principles for ethical AI, from IBM CEO Ginni Rometty” by Alison DeNisco. January 17, 2017, Tech
Republic http://www.techrepublic.com/article/3-guiding-principles-for-ethical-ai-from-ibm-ceo-ginni-rometty/
• "Transparency and Trust in the Cognitive Era" January 17, 2017 Written by: IBM THINK Blog
https://www.ibm.com/blogs/think/2017/01/ibm-cognitive-principles/
• "Ethics and Artificial Intelligence: The Moral Compass of a Machine“ by Kris Hammond, April 13, 2016.
Recode. http://www.recode.net/2016/4/13/11644890/ethics-and-artificial-intelligence-the-moral-compass-of-a-
machine
107. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Last bit: I promise
• "The importance of human innovation in A.I. ethics" by John C. Havens. Oct. 03, 2015
http://mashable.com/2015/10/03/ethics-artificial-intelligence/#yljsShvAFsqy
• "Me, Myself and AI" Fjordnet Limited 2017 - Accenture Digital.
https://trends.fjordnet.com/trends/me-myself-ai
• "Testing AI concepts in user research" By Chris Butler, Mar 2, 2017. https://uxdesign.cc/testing-ai-
concepts-in-user-research-b742a9a92e55#.58jtc7nzo
• "CMU prof says computers that can 'see' soon will permeate our lives“ by Aaron Aupperlee. March
16, 2017. http://triblive.com/news/adminpage/12080408-74/cmu-prof-says-computers-that-can-
see-soon-will-permeate-our-lives
• “The business case for augmented intelligence” by Nancy Pearson, VP Marketing, IBM Cognitive.
https://medium.com/cognitivebusiness/the-business-case-for-augmented-intelligence-
36afa64cd675#.qqzvunakw
108. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Definition: Artificial Intelligence
• Artificial intelligence (AI) is intelligence exhibited by machines.
• In computer science, an ideal "intelligent" machine is a flexible rational agent that
perceives its environment and takes actions that maximize its chance of success
at some goal.[1] Colloquially, the term "artificial intelligence" is applied when a
machine mimics "cognitive" functions that humans associate with other human
minds, such as "learning" and "problem solving".[2]
• Capabilities currently classified as AI include successfully understanding human
speech,[4] competing at a high level in strategic game systems (such as Chess
and Go[5]), self-driving cars, and interpreting complex data.
Wikipedia: https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-Intelligent_agents-1
109. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Definition: The Singularity
• If research into Strong AI produced sufficiently intelligent software, it might be able to reprogram
and improve itself. The improved software would be even better at improving itself, leading to
recursive self-improvement.[245] The new intelligence could thus increase exponentially and
dramatically surpass humans. Science fiction writer Vernor Vinge named this scenario
"singularity".[246] Technological singularity is when accelerating progress in technologies will
cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and
control, thus radically changing or even ending civilization. Because the capabilities of such an
intelligence may be impossible to comprehend, the technological singularity is an occurrence
beyond which events are unpredictable or even unfathomable.[246]
• Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in
digital technology) to calculate that desktop computers will have the same processing power as
human brains by the year 2029, and predicts that the singularity will occur in 2045.[246]
Wikipedia: https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-Intelligent_agents-1
110. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Definition: Machine Learning
• Ability for system to take basic knowledge (does not mean simple or non-complex)
and apply that knowledge to new data
• Raises ability to discover new information. Find unknowns in data.
• https://en.wikipedia.org/wiki/Machine_learning
More Definitions:
• Algorithm: a process or set of rules to be followed in calculations or other problem-
solving operations, especially by a computer.
https://en.wikipedia.org/wiki/Algorithm
• Natural Language Processing (NLP):
https://en.wikipedia.org/wiki/Natural_language_processing