[maksimum]

Instead of Institutional Review Boards approving experiments we could have Algorithm Validation Boards. Hold out a portion of your dataset for validation, and have the algorithm designers examine the outcomes in communication with ethicists, lawyers, and business strategists.

I don't think the two hands can properly "validate" algorithms without communicating. The algorithm designer can maximize AUC, but what if one <group>'s class is 95% label A; the designer always predicts A for <group>. How bad is ALWAYS missing 5% for label B? If you can put a price on it, then the developer can build it into the algorithm. But if the price is difficult to accurately estimate, or non-monetary qualities are desirable, it may be hard to build them into the classifier ahead of time. On the other hand if the cost of perfect <hard to quantify criterion> reduces AUC significantly, algorithm designers need to communicate that...