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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 below to join our mailing list!
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!
Join the meeting on the following Zoom link: https://bristol-ac-uk.zoom.us/j/96026442410
You can see the write ups of previous meetings here!
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.)?
2nd April - The Political Economy of Death in the Age of Information: A Critical Approach to the Digital Afterlife Industry#
16th April#
Material: TBC
30th April - AI in Education Special#
Material: TBC
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 still to be determined (weâll be asking our community what works best for them as we did with our last bookclub) but the rough schedule looks something like meeting on Zoom at 4-5pm UK time on the following dates:
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 you have feedback about this schedule (e.g. you canât make this time and would attend if we changed to a different time, or have noticed weâve mistakenly put a talk during a public holiday) then please let us know either via email or commenting on the Slack thread.
Past Meetings#
You can see a record of what we have discussed previously here.