There is too much performative expression of concern and not enough discussion of actual factors. The problems are basically the same as for human bias, and the

There is too much performative expression of concern and not enough discussion of actual factors. The problems are basically the same as for human bias, and the automation of the process gives us an opportunity to examine them transparently. Having lacrosse or sailing as a hobby on a CV is probably correlated with job performance, because those things are independently correlated with wealth and greater educational opportunities earlier in life. It's pretty simple to exclude them from a logistic regression. But what about education? A degree from an Ivy league university is correlated with job performance for both good and unfair reasons, and the correlation is very noisy. Which variables should be allowed? Which ones does the human system use, and how are they weighted? What causal models are appropriate? I don't know the answers, but the conversation often doesn't even happen at this level. And this is just at the level of transarent models, like graphical probablistic models or structural equation modeling. I generally think there's too much AI alarmism, but in this case I think banning non-linear machine learning methods using large high-dimensional training sets might be appropriate.