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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.
11th June: Chapter 1 - Introduction (34 pages)
18th June: Chapter 2 - How predictive AI goes wrong (24 pages)
2nd July: Chapter 3 - Why canât AI predict the future? (39 pages)
16th July: Chapter 4 - The Long Road to Generative AI (51 pages)
23rd July: Chapter 5 - Is Advanced AI an Existential Threat? (27 pages)
6th August Chapter 6 - Why canât AI fix social media? (48 pages)
13th August: Chapter 7 - Why do myths about AI persist? (31 pages)
20th August: Chapter 8 - Where do we go from here? (27 pages)
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.
Past Meetings#
22th January - Data Ethics New Yearâs Resolutions Special#
As very optional reading material, you could look at our previous new yearâs resolutions from 2024, 2023, and 2022.
5th February - âItâs Not Exactly Meant to Be Realisticâ: Student Perspectives on the Role of Ethics In Computing Group Projects#
Discussion Questions:
How do we define ethics in the context of computing education? Is it just doing the right thing or should it involve explicitly making injustices visible?
What do you think of the authorsâ recommendations for designing a project based computing course with ethics learning and practise in mind?
How can we make ethics be a functional requirement of good DS/SE practises in industry?
19th February - OpenAI Furious DeepSeek Might Have Stolen All the Data OpenAI Stole From Us#
Discussion Questions:
Under what circumstances do you think training AI models using publicly available internet materials is fair use? How should copyright come into this? Should these companies be publicly owed as a way to nationalise or democratise AI, since the models are trained on publically available data?
What do you think best practices should be for model distillation (i.e. one model learning off another)? Is it really stealing if OpenAI trained their model using vast amounts of publicly available data collected through web scraping?
DeepSeek was trained more cost effectively and with less powerful hardware but still performed as well as OpenAIâs model, attributed in part to its new architecture rather than just throwing more data + compute at the problem. Do you think that constrained environments can generally be a good catalyst for innovation?
5th March - International Womenâs Day Special#
Material link This meeting weâll play the above short video at the start as a primer before going into breakout rooms for discussions. If you enjoy the above video and want more, you should buy the speakerâs book Weapons of Math Destruction by Cathy OâNeil
Discussion Questions:
Can you think of a kind of implicit bias that affects you?
Have you ever been trained in the effect of implicit bias? Was it effective? How could it be improved?
What sorts of power affect peoplesâ rights to speak out about implicit bias (e.g. access to legal representation, transparency in decision making etc.)?
19th March - Alphafold: The Most Useful Thing AI Has Ever Done#
Discussion Questions:
Who âownsâ the knowledge generated by AlphaFold, and should there be special patent considerations for discoveries made primarily via AI-driven insights, such as big pharmaceutical companies discovering new drug compounds?
How should researchers and policymakers respond when the modelâs predictions can no longer be fully understood or straightforwardly verified by humans?
Does lack of understanding about how AI systems work/glossing over how intricate these models are harm scientific development by leading too many people to just âtry AIâ?
2nd April - The Political Economy of Death in the Age of Information: A Critical Approach to the Digital Afterlife Industry#
Discussion Questions:
Of the different kinds of services within DAI (section 3.1, page 6; Information Management Services, Posthumous Messaging Services, Online Memorial Services and Re-creation Services); Which have you encountered? Which are you ok with? Which would you use? Which do you want to ban or heavily regulate?
The paper focusses a lot on the potential for DAI to threaten/violate human dignity - what other concerns would like to be highlighted in the discussion of these âtechnologiesâ?
How would you regulate these technologies in a way which respects individual autonomy, the wishes of their loved ones and accounting for rapid development of technology in this area?
16th April - Understanding and supporting the mental health and professional quality of life of academic mental health researchers: results from a cross-sectional survey#
Discussion Questions:
What kinds of support and training should researchers, supervisors and other university staff be receiving? Where should it come from (universities, funding bodies etc.)
While probably unavoidable, is it ethical to have researchers with mental health conditions working on mental health research?
What would the next step be to enhance understanding of mental health among mental health researchers? (e.g. maybe analysis of the financial impact of absenteeism among the workforce to make the economic argument for support).
30th April - AI in Education Special#
Material: UK announces AI funding for teachers: how this technology could change the profession
With additional optional reading: Pedagogy of the Depressed
Session will be introduced by Dr Lyndsay Grant, Lecturer in Education at the University of Bristol.
Discussion questions:
What are your experiences with using AI to teach (either yourself or others)?
AI is promised to help alleviate teachersâ unsustainable workload and thereby address a crisis in teacher recruitment and retention. What other responses to this problem might there be and why are they not being pursued?
Who is accountable for the quality of education and resources generated through the use of AI? Who is accountable for the safe and ethical use of AI in schools and are they equipped to take on this role?
You can see a record of what we have discussed previously here.