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/

1. HICSS
Ethical and legal implications raised by
Generative AI and Augmented Reality
in the workplace.
Jim Spohrer
Retired Industry Executive (Apple, IBM)
Board of Directors (ISSIP, ServCollab)
UIDP Senior Fellow
Questions: spohrer@gmail.com
Twitter: @JimSpohrer
LinkedIn: https://www.linkedin.com/in/spohrer/
Slack: https://slack.lfai.foundation
Presentations online at: https://slideshare.net/spohrer
Humankind: A Hopeful History
By Dutch Historian, Rutger Bregman
<- Thanks
To Ray Fisk
For suggesting
this book, see
My summary here.
See also
ServCollab.
Nonzero: The Logic of Human Destiny
By USA Journalist, Robert Wright
Thanks to Souren Paul for the opportunity
To discuss AI and IA.
January 3, 2024

4. Optimistic Realistic
Knowing
Doing
How to keep up with accelerating change? Follow a diverse collection of people… make up dimensions meaningful to you!
Sadly for me… my brain is biased into thinking I can understand older, white, males the best… maybe AI can help overcome!

5. In Memory of Douglas Engelbart (1925-2013)

7. 1960 1980 2000 2020 2040 2060 2080
$1,000,000,000,000
(Trillion)
$1,000,000
(Million)
$1,000,000,000
(Billion)
$1,000
(Thousand)
$1
GDP/Employee
Trend
Estimating Knowledge Worker Productivity
Based on USA
Historical Data
Year Value
1960 $10K
1980 $33K
2000 $78K
2020. $151K
2023 $169K
Cost of computation goes down by 1000x every 20 years (left to right diagonals), driving knowledge worker productivity up.

8. Some paths to becoming 64x smarter:
Improving learning and performance
• 2x from Learning sciences (methods)
• Better models of concepts
• Better models of learners
• 2x from Learning technology (tools)
• Guided learning paths
• Elimination (?) of “thrashing”
• 2x from Quantity effect (overlaps)
• More you know, faster (?) you go
• Advanced organizers
• 2x from Lifelong learning (time)
• Longer lives and longer careers
• Keeps “learning-mode” activated
• 2x from Early learning (time)
• Start earlier: Challenged-based approach
• STEM-2D in K-12 (SSME+DAPP Design of Smart Service Systems)
• 2x from Cognitive systems (performance support)
• Technology & Infrastructure Interactions
• Organizations & Others Interactions

9. Responses to questions
• What should the research community be focused on to
mitigate the risks and harms?
• Deeper Understanding
• Generate capabilities and limitations
• Shift to generate-test-and-debug architecture with dynamic
memory (episodic memory)
• People can often provide a post-hoc reasoning to explain an
answer given, even if not sure of the process of getting to the
answer

10. Responses to questions
• Explainability, Bias, and Reliability: One issue that raises ethical and legal questions is the black-box nature of current generative AI systems: the
distributed,parametric nature of their knowledge representation, embedded in the huge numbers of weights in the model, makes it more difficult (or impossible) to
interrogate the nature of their reasoning or sources of their conclusions. The explanation of how they derive their results is not easily accessible. [Jim/Alex]
• Given this complication, how can we trust applications based on such models to be reliable and unbiased?
• What approaches might be taken to mitigate the risks imposed by lack of explicit knowledge representation
1.We can’t trust AI responses
2.We can work to develop G-T-D architectures

11. Responses to questions
• IP ownership and Liability: Large Language Models are generally trained on data that is available on the web, but although it is available to read, much of it is copyrighted
work. The models are thus capable of generating content that violates copyright. And even if the output produced is outside the scope of copyright, the act of using the copyrighted
content to train a model, without permission to use it in that way may raise both ethical and legal concerns (Natalia/Jim)
• If a generative AI system, prompted by an end user, generates text or images that violate copyright, who is / should be held liable?
• nobody, as long as the use of the output falls within fair use?
• the end user who provided the prompt that led to the infringing behavior? (even if they don’t know that it infringes?
• the system developers?
• Even if the system’s output does not violate copyright, does using other people’s IP without permission or credit violate legal or ethical rules? And if so, what should the
remedy be? Are content owners entitled to compensation when their content is used to train a model?
1.The vendors are responsible
2.Historically vendors have to implement a process

13. Responses to questions
• Disinformation and other nefarious uses: The ability to generate prodigious amounts of content that is very compelling but inaccurate would appear to be a great
enabler of disinformation campaigns. [Dan/Alex]
• How serious is this risk, above and beyond the current techniques for spreading disinformation, and what can / should be done about it?
1.Bad actors have to be caught and punished

14. Responses to questions
• Worker displacement: Numerous sources predict that AI is now poised to replace many knowledge-worker roles, eliminating many white collar jobs. This includes
more administrative work, such as contract drafting, but also more creative work, turning a plot outline into a detailed sitcom script. [Jim/Natalia/Dan/Alex]
• What ethical considerations should guide the AI scientists, application developers, employers and other ecosystem players to protect workers who may be
harmed by this technology?
• Will there be - as with previous technological waves - enough new jobs created to make up for those that are taken over? What can and should be done to
bring these impacts into balance.
1.When we use AI – disclose key information
2.Yes, there will be many more jobs,
but the people who lose their jobs
can and should demand better service systems

15. Topics for discussion: Jim’s Questions
• Beyond Language for Communications
• Here is how my AI, using my digital twin of you, predicted that you would respond to my
request – could you please ask your digital twin of yourself to check this response and
suggest improvements?
• How to keep up with accelerating change?
• Who do you follow? What two main dimensions do you try to balance? Hype-buster <->
Super-Optimist, Doing (Try this) <-> Knowing (News)
• How to verify results of generative AI?
• How to deal with verification challenge? Run Open AI ChatGPT 3.5, Google Bard (waiting for
Gemini), Anthopic’s Claude, Microsoft Bing power by Open AI ChatGPT 4 – in parallel and
have them critique each others’ responses (where they agree, where and how they differ) – if
possible, also run Meta and open source to compare (Generate-Test-and-Debug)
• How to deeply understand how generative AI works?
• Monkey’s at the typewrite in high dimensional spaces that map to low dimensional spaces
• One dimensional space is time – what comes next? (Predictors, unsupervised learning)
• Two dimensional space is what people gravitate towards in business schools (Sorters,
supervised learning)

16. Metaverse

17. Thank-you
Organizers
& Panelists

18. Jim Spohrer is a Silicon Valley-based Advisor to industry, academia, governments,
startups and non-profits on topics of AI upskilling, innovation strategy, and win-
win service in the AI era. Most recently with a consulting team working for a top
10 market cap global company, he contributed to a strategic plan for a globally
connected AI Academy for achieving rapid, nation-scale upskilling with AI. With
the US National Academy of Engineering, he co-led a 2022 workshop on “Service
Systems Engineering in the Era of Human-Centered AI” to improve well-being.
Jim is a retired IBM Executive since July 2021, and previously directed IBM’s open-
source Artificial Intelligence developer ecosystem effort, was CTO IBM Venture
Capital Group, co-founded IBM Almaden Service Research, and led IBM Global
University Programs. In the 1990’s at Apple Computer, as a Distinguished Engineer
Scientist and Technologist, he was executive lead on next generation learning
platforms. In the 1970’s, after his MIT BS in Physics, he developed speech
recognition systems at Verbex (Exxon) before receiving his Yale PhD in Computer
Science/AI. In 1989, prior to joining Apple, he was a visiting scholar at the
University of Rome, La Sapienza advising doctoral students working on AI and
Education dissertations. With over ninety publications and nine patents, he
received the Christopher Lovelock Career Contributions to the Service Discipline
award, Gummesson Service Research award, Vargo and Lusch Service-Dominant
Logic award, Daniel Berg Service Systems award, and a PICMET Fellow for
advancing service science. Jim was elected and previously served as Linux
Foundation AI & Data Technical Advisory Board Chairperson and ONNX Steering
Committee Member (2020-2021). Today, he is a UIDP Senior Fellow for
contributions to industry-university collaborations, and a member of the Board of
Directors of the International Society of Service Innovation (ISSIP) and ServCollab.
Jim Spohrer, Advisor
Retired Industry Executive (Apple, IBM)
UIDP Senior Fellow
Board of Directors, ServCollab
Board of Directors, ISSIP.org
Changemaker Priorities
1. Service Innovation
2. Upskilling with AI
3. Future Universities
4. Geothermal Energy
5. Poverty Reduction
6. Regional Development
Competitive Parity
Technologies
1. AI & Robotics
2. Digital Twins
3. Open Source
4. AR/VR/XR
5. Geothermal
6. Learning
Platforms

19. We get the future we invest in:
AI tools to experiment with today
• #1 Magic Eraser
• #2 Craiyon
• #3 Rytr And GPT-3, ChatGPT, GPT-4, Bing
• #4 Thing Translator
• #5 Autodraw
• #6 Fontjoy
• #7 Talk to Book
• #8 This Person Does Not Exist
• #9 Namelix
• #10 Let's Enhance
Thanks to @TessaRDavis
for compiling this list:
“Service providers
will not be replaced by AI,
but trusted service providers
who use AI (well and responsibly)
will replace those who don’t.”
National Academy - Service Systems and AI 19
Try at least two
from the list
as soon as possible
What do you think?
, DALL-E and Stable Diffusion
Every person in a role in an organization is a service provider.
1/4/2024

20. Two disciplines: Two approaches to the future
Artificial Intelligence is almost seventy-years-old discipline in computer
science that studies automation and builds more capable technological
systems. AI tries to understand the intelligent things that people can do
and then does those things with technology. (https://deepmind.com/about “...
we aim to build advanced AI - sometimes known as Artificial General Intelligence (AGI) - to
expand our knowledge and find new answers. By solving this, we believe we could help
people solve thousands of problems.”)
Service science is an emerging transdiscipline not yet twenty-years- old
that studies transformation and builds smarter and wiser socoi-
technical systems – families, businesses, nations, platforms and other
special types of responsible entities and their win-win interactions that
transform value co-creation and capability co-elevation mechanisms
that build more resilient future versions of themselves – what we call
service systems entities. Service science tries to understand the
evolving ecology of service system entities, their capabilities,
constraints, rights, and responsibilities, and then then seeks to improve
the quality of life of people (present/smarter and future/wiser) in those
service systems.
Artificial Intelligence
Automation
Generations of machines
Service Science
Transformation
Generations of people
(responsible entities)
Service systems are dynamic configurations of people,
technology, organizations, and information, connected
internally and externally by value propositions, to other
service system entities. (Maglio et al 2009)