>No amount of white-boxing or model transparency will get around this trilemma. The sooner the industry comes to grips with it and learns to explicitly wield it

>No amount of white-boxing or model transparency will get around this trilemma. The sooner the industry comes to grips with it and learns to explicitly wield it when required, the better. Agreed. The optima of multiple criterion will essentially never intersect. But for Amazon, there is no evidence the tool was accurately selecting the best candidates. They themselves never said it was. After all, altering word choices in a trivial way dramatically affects ranking. On the points you mention, why should we assume their data was relevant or their fitness function even doing what they thought? If they were naive enough, they could just be building something that predicts what the staff of an e-commerce monopoly in a parallel universe will look like. The most likely story is that they failed at what they were doing. Part of that failure happened to be controversial and so got unwanted attention. I would guess there were quite a few incredible correlations the tool "discovered" that did not get to press. At any rate, their recommendation engine is more important and has been worked on longer yet it is conspicuously flawed. When their recommendation tool inspires awe then maybe we could take their recruiting engine seriously enough to imagine it has found deep socio-psychological-genetic secrets.