My session: programs.sigchi.org/facct/2025/p...
Agonistic Image Generation (SFL): doi.org/10.1145/3715...
Crockett Lab: crockettlab.org

Also, I finally presented! I'm biased but my session was one of the best, so please check out everyone's papers in "Normative Challenges to AI". My SFL peers also presented on the last day which is an amazing accomplishment as undergrads! FAccT was a great end to my 1st PhD year!

I think most FAccT attendees are battling these contradicting perspectives within themselves, but I'm worried how much can actually get done until this contradiction is resolved. What gives me hope is that FAccT is one of the only AI venues that takes this head on, so let's see 🤞🏿

For genAI refusers, the eco-socio-political problems of genAI are already so evident that studying its capabilities doesn't address the root. Rather, AI ethics research should shift (in topic and practice) to refusing genAI narratives and step back to examine what AI should be in the first place.

In this framing lies 2 major camps driving what AI ethics looks like in a time of technofascism: genAI realists and genAI refusers. For genAI realists, AI ethics should be studying genAI to gain a clearer understanding of its risks and actual capabilities to counter hype and inform policy.

Oftentimes, these studies oversimplify complex human tasks such as generosity or domain knowledge to conveniently set up genAI as superior to humans. This research framing is also informed by messaging + investments by tech superpowers that genAI can and will surpass human limitations.

Especially in the face of anti-ethics political environment in the US, continuing impactful work in AI ethics requires solidarity, tact, and limits to concessions. I will add notable papers and ideas below!

And a particular shout out to my first year mentor Kentrell for his talk about the BI Smartlink app. The suffering of countless of migrants this ICE app enables shook the room to its core but it's necessary for technologists to see how our work can have dire consequences. dl.acm.org/doi/10.1145/...

Some filled me with hope like the LatAm co-op (s/o Codigo Libre + MariaLab), left me with deep dread (OpenAI is working with AFRICOM sites), and some a pragmatic middle (Nathalie Smuha's critique of the EU AI Act). But all of these examples highlighted the need for localized sociopolitical analysis.

It doesn't matter if these metaphors actually describe AI, but rather how they illustrate the social relationships to be mediated by policy, tools, and evaluations. This focus reminds me the AI creative writing paper from my colleagues, Alicia Guo and Shreya Sathyanarayanan: arxiv.org/pdf/2411.03137

Forgot the @ of my collaborator @biira-b.bsky.social!

Finally, if you would like to learn more about our work check out our preprint (africarxiv.ubuntunet.net/entities/pub...) and/or attend my presentation at @facct.bsky.social , this Thursday @ 9am CET in the Normative Challenges to AI session (programs.sigchi.org/facct/2025/p...)

Thank you to my co-advisors @axz.bsky.social and Leilani Battle! I'm also so appreciative of my colleagues Eman and Biira for their support. Last and certainly not least, huge thanks to @chinasa.bsky.social for joining this project as a SME!

We hope that our framework encourages further engagement with African perspectives throughout the global data ethics community. We are also very eager to learn how African data science communities see our framework reflected or not reflected in their practices.

Finally, we offer a simple case study for each major principle to demonstrate African data ethics discourses in practice through a variety of African data science actors such as Masakhane NLP, miners of the Democratic Republic of Congo, and the National Institute for Statistics of Rwanda.

We also explore how our framework fits in with other notable data ethics frameworks. While all frameworks highlight striving for common good and consensus-building as normative prioirities, we find African data ethics uniquely use these principles to call for material redress for data harm.

These six-high level principles are scafolded by 19 minor principles. We surface the strong decolonial and anti-neocolonial critiques of the global data science ecosystem and passionate reclaimations of African innovation to chart a more self-determined future for African responsible data science.

With document thematic analysis, we constructed a 6-principle framework:
✊🏿Challenge Power Asymmetries
🙋🏿‍♀️ Assert Data Self-Determination
🖥 Invest in Local Data Institutions and Infrastructures
🫂 Utilize Communalist Practices
🤲🏿 Center Communities on the Margins
💚 Uphold Common Good