Can algorithms be fair ?
I don t often write long reviews of single papers. Maybe I should. Stochastic Parrots have finally launched into mid-air. The paper at the heart of the huge brouhaha involving Google s resignating of Timnit Gebru back in December is now available, and will appear at FAccT 2021. Reading papers in this space is always a tricky Continue reading On stochastic parrots
https://www.theatlantic.com/ideas/archive/2020/06/what-because-of-sex-really-means/613099/ https://www.stanfordlawreview.org/online/the-many-meanings-of-because-of/ I ve been reading a number of analyses of the landmark Gorsuch decision in the LGBTQ discrimination case. The articles linked above are very helpful in this regard, but I couldn t help but also notice a very computational argument in…
Centering One of the topics of discussion in the broader conversation around algorithmic fairness has been the idea of decentering: that we should move technology away from the center of attention as the thing we build to apply to people and towards the sides as a tool to instead help people. This Continue reading On centering, solutionism, justice and (un)fairness.
The conference is over, and I m more exhausted than I thought I d be. It was exhilarating. But the job of a paper summarizer never ends, and I am doing this exercise as much for my own edification as anyone else s 🙂 The theme of this session is a little more spread out, but all the Continue reading FAT* Papers: Fairness Methods
Me (in the hallway at FAT*): Hi [person]: Oh hi, how re you doing? pause [person];. So . when s the next post going to be up? Which brings us to Session 3. Kate Crawford gave a talk at NIPS (NeurIPS?) 2017 on harms of representation that has had a profound influence on my thinking about fairness. We re Continue reading FAT* Papers: Profiling and Representation
I ve made it to Session 2 of my series of posts on the FAT* conference. If you build it they will come. How should we build systems that incorporate all that we ve learnt about fairness, accountability and transparency. How do we go from saying this is a problem to saying Here s a solution ? Three of Continue reading FAT* Papers: Systems and Measurement
The FAT* Conference is almost upon us, and I thought that instead of live-blogging from the conference (which is always exhausting) I d do a preview of the papers. Thankfully we aren t (yet) at 1000 papers in the proceedings, and I can hope to read and say something not entirely stupid (ha!) about each one. I Continue reading FAT* Papers: Framing and Abstraction
(Update: apparently as a result of all the pushback from activists, the ACLU and others, the rollout of the new tool has been pushed back at least 6 months) The Pennsylvania Commission on Sentencing is preparing a new risk assessment tool for recidivism to aid in sentencing. The mandate for the commission (taken from their Continue reading On the new PA recidivism risk assessment tool
These days, there are lots of fairness-aware classification algorithms out there. This is great! It should mean that for any task you want to pursue you can try out a bunch of fair classifiers and pick the one that works best on your dataset under the fairness measure you like most. Unfortunately, this has not Continue reading Benchmarks and reproducibility in fair ML
Predictive policing describes a collection of data-driven tools that are used to determine where to send officers on patrol on any given day. The idea behind these tools is that we can use historical data to make predictions about when and where crime will happen on a given day and use that information to allocate Continue reading Models need doubt: the problematic modeling behind predictive…