Algorithmic Fairness

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Can algorithms be fair ?

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On stochastic parrots
Jan 23, 2021 · original
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 business. The world of algorithmic fairness is much broader than either algorithms or fairness (and stay tuned for my next post on this). Contributions come in many forms, from many different disciplinary and methodological traditions, and are situated in different contexts. Identifying the key contributions of a paper and how they broaden the overal discussion in the community can be tricky, especially if we define contributions based on our own traditions. And then we have to critique a paper on its own terms, rather than in terms of things we want to see. And d
On “Bostock vs Clayton County” and algorithmic discrimination.
Jun 17, 2020 · original
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 Gorsuch’s reasoning that might be relevant for algorithmic discrimination. The question at hand was whether firing someone because they were gay or trans could be viewed as being “because of sex” as per the Civil Rights Act. The opposing argument was that they weren’t fired because of their sex (or gender to be more precise) but because they were gay or trans, and since sexual orientation/gender identity was not protected in the Civl Rights Act explicitly, it’s not a violation. The argument from
On centering, solutionism, justice and (un)fairness.
Jul 29, 2019 · original
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 idea took me a while to understand, but makes a lot of sense. After all, we indeed wish to use “tech for good” — to help us flourish — the idea of eudaimonia that dates back to Aristotle and the birth of virtue ethics. We can’t really do that if technology remains at the center. Centering the algorithm reinforces structure; the algorithm becomes a force multiplier to apply uniform solutions for all people. And that kind of flattening – the treatment of all the same way – is what leads to procedural ideas of fairness as consistency, as well as systematically unequal treatment of those that
FAT* Papers: Fairness Methods
Feb 2, 2019 · original
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 papers are “tools” papers in the classic ML sense: trying to build widgets that can be useful in a more introspective processing pipeline. Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data The interaction between causality and fairness is getting steadily more interesting. In a sense it’s hard to imagine doing any kind of nondiscriminatory learning without causal models because a) observational data isn’t sufficient to determine bias without some understanding of causality, and b) causal modeling helps us understand where the sources of bias might be coming from (more on t
FAT* Papers: Profiling and Representation
Jan 30, 2019 · original
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 all familiar with harms that come from biased decision making — harms of allocation — but it’s a little harder to discuss what it means to face harm from a skew in representation. A few years ago we saw a series of papers that demonstrated that standard representations of text using methods like Word2Vec and GloVe could encode biases in the training corpora. But can we connect these harms directly to harms of allocation? In other words, to what extent can we attribute a harm of allocation to a skewed representation rather than distributional bias or bad metrics? Bias in Bios: A Case Study
FAT* Papers: Systems and Measurement
Jan 29, 2019 · original
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 the four papers in this session seek (in different ways) to address this question, focusing on both the data and the algorithms that make up an ML model. Beyond Open vs. Closed: Balancing Individual Privacy and Public Accountability in Data Sharing The paper by Meg Young and friends from UW makes a strong argument for the idea of a data trust. Recognizing that we need good data to drive good policy and to evaluate technology and also recognizing that there are numerous challenges — privacy, fairness, and accountabilty — around providing such data, not to mention issues with private vs pub
FAT* Papers: Framing and Abstraction
Jan 28, 2019 · original
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 spent a lot of time pondering how to organize my posts, and then realized the PC chairs had already done the work for me, by grouping papers into sessions. So my plan is to do a brief (BRIEF!) review of each session, hoping to draw some general themes. ( ed: paper links will yield downloadable PDFs starting Tuesday Jan 29) And with that, let’s start with Session 1: Framing and abstraction . Those who do not learn from history are doomed to repeat it. — George Santayana Those who learn from history are doomed to repeat it. — twitter user, about machine learning. 50 Years of Test (Un)
On the new PA recidivism risk assessment tool
Jun 11, 2018 · original
( 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 report — also see the detailed documentation at their site) is to (emphasis all mine): adopt a Sentence Risk Assessment Instrument for the sentencing court to use to help determine the appropriate sentence within the limits established by law…The risk assessment instrument may be used as an aide in evaluating the r elative risk that an offender will reoffend and be a threat to public safety .” (42 Pa.C.S.§2154.7) In addition to considering the risk of re- offense and threat to public safety, Act 2010-95 also permits the risk assessment instrument to be used to determi
Benchmarks and reproducibility in fair ML
Feb 19, 2018 · original
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 been the case. Even in the cases where code is available, the preprocessing of a specific data set is often wrapped into the algorithm, making it hard to reuse the code and hard to see what the impact of different preprocessing choices are on the algorithm. Many authors have used the same data sets, but preprocessed different ways and evaluated under different metrics. Which one is the best? In an effort to address some of these questions, we’ve made a repository and written an accompanying paper detailing what we’ve found. http://github.com/algofairness/fairness-comparison We’ve m
Models need doubt: the problematic modeling behind predictive policing
Jan 5, 2018 · original
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 officers appropriately. On the one hand, predictive policing tools are becoming ever more popular in jurisdictions across the country. They represent an argument based on efficiency: why not use data to model crime more effectively and therefore provision officers more usefully where they might be needed? On the other hand, critiques of predictive policing point out that a) predicting crimes based on arrest data really predicts arrests and not crimes and b) by sending officers out based on predictions from a model and then using the resulting arrest data to update the model, yo

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