Model fairness

Think pieces on fairness in models in practice

• How big data is unfair
• Visualisation of ML discrimination by Google staffers (Hardt, Price, and Srebro 2016)
• Excavating AI The Politics of Images in Machine Learning Training Sets, by Kate Crawford and Trevor Paglen

Fairness and causal reasoning

Here’s a thing that was so simple and necessary I assumed it had already been done long before it was. (Kilbertus et al. 2017)

Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively.

Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from “What is the right fairness criterion?” to “What do we want to assume about the causal data generating process?” Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them.

Fairness trade-offs

There are certain impossibility theorems around what you can do here. In a certain sense the only fair model is no model at all. Who should our automated model extend a loan to? everyone! no-one! All other decision rules impinge upon the inpenetrable thicket of cause and effect and historical after-effects that characterise human moral calculus. No rule can, satisfy every ethical notion of fairness. Also practically it is impossible to give absolute certainty that unfair criteria are not leaking in to any given non-trivial model. However, that doesn’t mean you can’t fall short of the impossibility frontier on the side of unfairness (or indeed pointless inefficiency) if you don’t work at it.

Chris Tucchio, at crunch conf makes some points about marginalist allocative/procedural fairness and net utility versus group rights.

If we choose to service Hyderabad with no disparities, we’ll run out of money and stop serving Hyderabad. The other NBFCs won’t.

Net result: Hyderabad is redlined by competitors and still gets no service.

Our choice: Keep the fraudsters out, utilitarianism over group rights.

He does a good job of explaining some impossibility theorems via examples, esp (Kleinberg, Mullainathan, and Raghavan 2016). Note the interesting intersection of two types of classifications implicit in his model — uniformly reject, versus biased accept/reject, subject to capital constraints. I need to revisit that and think some more.

Han Zhao is an actual researcher in this area. Inherent Tradeoffs in Learning Fair Representations, including two of their own results Zhao et al. (2019); Zhao and Gordon (2019).

References