This is a direct and clear example of bias which made it easy to flag the ML algorithm. But what about ML algorithms that are inducing benefits to groups in less obvious contexts? What about groups that are not so easily identified as being protected classes by simple, human-understandable model features? What about cases where the features are just merely correlated with a subpopulation of a protected class? If we'r…
The article notes that Amazon's system rated down grads from two all-women's schools. But it immediately occurs to me to wonder what the algorithm did with candidates from heavily gender-imbalanced schools, which could be much harder to spot.
RPI's Computer Science department is about 85% male, while CMU's is just over 50% male. CMU's CS department is also considered one of the best in the world, and presumably any functional algorithm that cared about alma mater would respond to that. So if the bias ends up being "because of CMU's gender ratio, CMU grads with gender-unclear resumes are advantaged slightly less than otherwise would be", how on earth would someone spot that?
Once you're looking for it, you could potentially retrain with some data set like "RPI resumes, but we adjusted their gendered-words rate" and see if you get a different outcome on your test set. But that's both a labor intensive task, and one that's only approachable once you already know what you're looking for. And even if you do see a change, you'd still have to tease it out from a dozen other hypotheses like "certain schools have more organizations with gendered names, and the algorithm can't tell that those organizations are a proxy for school".
Of course, the counterpoint is that human decisions can't be scrutinized any better, and it's not entirely clear they're less arbitrary or more ethical. At a certain point algorithmic approaches are being scrutinized because they're slightly transparent and testable, so running them on a range of counterfactuals or breaking down their choices is hard rather than impossible. I suspect that's true, but it doesn't really comfort me - humans at least tend to misbehave along certain predictable axes we can try to mitigate, while ML systems can blindside us with all sorts of new and unexpected forms of badness.