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Everyone is welcome to get involved in Data Ethics Club, as much or as little as youâd like to! We would love to hear your point of view at our discussion groups, to have your support in organising or running a meeting, or to add your contributions to our reading list.
You donât need to be a data ethicist (weâre not!), or a data scientist - having a variety of different people is how we learn from each other. Itâs a friendly and welcoming group and we often have new people drop by, so why not try it?
We meet every other week for one hour on Zoom (Wednesdays, 1pm, UK time) to talk about something from the reading list. Out upcoming meeting dates are available below. If you would like to get email reminders about the content and dates for the next meeting then click above to join our mailing list! You can also join the DEC Slack by clicking here.
Please read our Code of Conduct before attending.
Upcoming meetings#
These are the meetings for the next academic term.
We will update the material and questions based on the previous weeksâ vote.
All meetings are held at 1pm UK time and last one hour. If you are in another timezone please use a time/date converter like this one to check your local time!
You can see the write ups of previous meetings here!
Summer Book Club#
For our summer 2025 bookclub, we will be reading AI Snake Oil: What artificial intelligence can do, what it canât and how to tell the difference by Arvind Narayanan and Sayash Kapoor.
On their website you can read the first chapter online for free, see what each chapter is about and see the suggested exercises and discussion prompts by the authors to get an idea of the kinds of conversation we might be having.
There are 8 chapters and the exact schedule is below. Weâll be meeting 4-5pm UK time on Microsoft Teams, click here to join.
If youâd like to be forwarded calendar invites to the session then you can let us know via email or by commenting on the Slack thread.
Discussion prompts below taken from the suggested exercises and discussion prompts by the authors, but should be rough guidelines of discussions, not the only thing you can talk about!
11th June: Chapter 1 - Introduction (34 pages)#
Suggested discussion prompts:
What do âeasyâ and âhardâ mean in the context of AI? Does it refer to computational requirements, or the human effort needed to build AI to perform a task, or something else? And what does easy/hard for people mean?
Based on your definitions of these terms, pick a variety of tasks and try to place them on a 2-dimensional spectrum where the axes represent peopleâs and computersâ ease of performing the task. What sort of relationship do you see?
The text gives many examples of AI that quietly work well, like spellcheck. Can you think of other examples? What do you think are examples of tasks that AI canât yet perform reliably but one day will, without raising ethical concerns or leading to societal disruption?
18th June: Chapter 2 - How predictive AI goes wrong (24 pages)#
Suggested discussion prompts:
Predictive models make âcommon senseâ mistakes that people would catch, like predicting that patients with asthma have a lower risk of developing complications from pneumonia, as discussed in the chapter. What, if anything, can be done to integrate common-sense error checking into predictive AI?
Think about a few ways people âgameâ decision-making systems in their day-to-day life. What are ways in which it is possible to game predictive AI systems but not human-led decision making systems? Would the types of gaming you identify work with automated decision-making systems that do not use AI?
In which kinds of jobs are automated hiring tools predominantly used ? How does adoption vary by sector, income level, and seniority? What explains these differences?
2nd July: Chapter 3 - Why canât AI predict the future? (39 pages)#
Suggested discussion prompts:
Suppose a research group at a big tech company finds that it can build a model to predict which of its users will be arrested in the next year, based on all the private user data that it stores, such as their emails and financial documents. While far from perfectly accurate, it is more accurate than any model that uses public data alone.
Does it seem plausible that a model like this might work in any meaningful sense? If so, what signals might it be picking up on?
What laws, rules, or norms should govern companiesâ ability to undertake research projects of this sort?
Is there any ethical and responsible way in which technology like this can be put to use, or should we as a society reject such uses of prediction?
What, if anything, prevents a company from partnering with police departments in your country to use such a predictive model for surveillance of individuals deemed high risk?
16th July: Chapter 4 - The Long Road to Generative AI (51 pages)#
Suggested discussion prompts:
Spend at least an hour using a chatbot for learning. Reflect on your experience and discuss it with your peers. What worked well, and what didnât? Do you plan to continue to use chatbots for learning?
Generative AI is built using the creative output of journalists, writers, photographers, artists, and others â generally without consent, credit, or compensation. Discuss the ethics of this practice. How can those who want to change the system go about doing so? Can the market solve the problem, such as through licensing agreements between publishers and AI companies? What about copyright law â either interpreting existing law or by updating it? What other policy interventions might be helpful?
Discuss the environmental impact of generative AI. What, if anything, is distinct about AIâs environmental impact compared to computing in general or other specific digital technologies with a large energy use such as cryptocurrency?
23rd July: Chapter 5 - Is Advanced AI an Existential Threat? (27 pages)#
Suggested discussion prompts:
In AI safety policy, entrenched camps have developed, with vastly divergent views on the urgency and seriousness of catastrophic risks from AI. While research and debate are important, policymakers must make decisions in the absence of expert consensus. How should they go about this, taking into account differences in beliefs as well as values and stakeholdersâ interests?
Make predictions on the forecasting website Metaculus on a few AI- and AGI-related questions. Be sure to read the âresolution criteriaâ carefully. What data or information did you consider? What do you think of the community predictions? Discuss with your peers.
As of 2024, there have been a few attempts to automate AI research. Read some of this work. What set of activities are researchers trying to automate? Assess how close they are to their goal. What are the implications of being able to automate AI research?
13th August: Chapter 7 - Why do myths about AI persist? (31 pages)#
Suggested discussion prompts:
One difference between AI research and other kinds of research is that most AI research is purely computational, and doesnât involve (for instance) experiments involving people or arduous measurements of physical systems. In what ways does this make it easier to have confidence in the claims of AI research? In what ways does it make it harder?
What techniques do you personally use to stay grounded when you hear of seemingly amazing AI advances in the news? Discuss with your peers.
What are some ways to improve accountability for companies making unsubstantiated claims? These could include legal remedies as well as non-legal approaches.
20th August: Chapter 8 - Where do we go from here? (27 pages)#
Suggested discussion prompts:
The chapter makes the point that broken AI appeals to broken institutions. What are some examples of broken institutions enamored by other dubious technologies? Is there something about AI, as opposed to other technologies, that makes it liable to be misused this way?
What impact do you think AI will have on your chosen or intended profession in the next five to ten years? What levers do we have to steer this impact in a way that is positive for society?
Look up some examples of AI-related legislation or regulation recently enacted or being debated in your country. Discuss the pros and cons of specific actions and proposals, as well as the overall approach to AI policymaking.
Past Meetings#
You can see a record of what we have discussed previously here.