The document discusses the impact of AI on society, highlighting both positive aspects, such as improved transportation and personalized education, and negative aspects, including ethical concerns and environmental impacts. It emphasizes the importance of designing human-AI interactions that prioritize human needs and explainability, while also addressing socioeconomic factors influenced by AI. The insights are framed within the context of technological advancements and the evolving landscape of AI capabilities.

1. Alan Dix
https://alandix.com/
@alanjohndix
Human-Centred AI

4. impact of AI
what AI does
how AI shapes society

5. c.f. cars
what cars do
design – automotive and highways
+ve transport, independence
–ve accidents, speeding, drunk drivers
how cars shape society
– suburban sprawl
– car poverty
– global warming

6. impact of AI
what AI does
how AI shapes society

7. designing human-AI interactions

8. touchstone phrases
alien intelligence
not as we know it
appropriate intelligence
fitting within interaction framework
optimise the human–AI system, not just the AI
sufficient reason
explanations we would accept from a human being
critical for AI explainability

9. styles of collaboration
Servant – we tell AI what to do (explicitly)
Master – AI tells us what to do, e.g. Uber
Symbiosis – using complementary abilities
“Man-Computer Symbiosis”
J. C. R. Licklider, March 1960
N.B. boundaries may be blurred
– e.g. map interfaces, recommendations

10. synergistic AI
design principles for both AI and UI change
… epistemic interaction

11. ACCESS
DOING
IT RIGHT
POLICY
AI and Social Justice
Clara Crivellaro

12. ACCESS
DOING
IT RIGHT
POLICY

13. ACCESS
DOING
IT RIGHT
POLICY

14. ACCESS
DOING
IT RIGHT
POLICY
DOING
IT RIGHT

15. ACCESS
DOING
IT RIGHT
POLICY

16. impact of AI
what AI does
how AI shapes society
economy, education, environment
politics, power
democracy, digital poverty

17. AI … why now?
1990 2000 2010 2020
1980
1970
1950 1960
AI winters

18. AI … why now?
1990 2000 2010 2020
Early
NN
web
search
recommender
systems
MapReduce
deep NN
AlphaGo
LLM
ChatGPT
Big Data

19. AI and Computation
deep NN were always known about
… what changed?
web (search, ecommerce)
=> big data
=> big data centres
big data + big computation => DNN
the new AI

20. new AI
BIG data + BIG computation
=
BIG business

21. Open Data
“I don’t know if any public sector
has necessarily cracked the nut on attracting the
right skills and capabilities,” …
“The commercial sector has,
because they’ve got the dollars to spend.”
Ian Bartram, Gartner

22. signs of hope

23. Meta’s open LLaMa
the model’s carbon
footprint has been reduced
to a seventh of GPT-3
the pretrained model will obviate
the need to train the model
… deploy the full model “using
only 16 NVIDIA V100 GPUs.”
~ $400,000

24. LORA – open and (relatively) cheap
reduce cost and carbon
low dimension
representation
of inner layers

25. generative AI enables niches
education
– personalised tutors => choice of materials
– changes what we need to learn
… not necessarily digital!
minority languages
health
– personalised medicine
– well-being and mental health
https://www.theguardian.com/world/2023/sep/11/sweden-says-back-to-basics-schooling-works-on-paper

26. Human-Centred AI
putting people first
Alan Dix
https://alandix.com/