No, that isn't what I meant. Older statistical methods were not complex and could be analyzed by a human to ensure they complied with legal and moral obligatio

No, that isn't what I meant. Older statistical methods were not complex and could be analyzed by a human to ensure they complied with legal and moral obligations. Newer approaches such as but not limited to neural networks create a model that is so complex that it is opaque. You cannot necessarily verify that it is complying with legal and moral obligations. This is fine when you are doing song recommendations. It is more of a problem when you are making decisions that could be seen as discriminatory. There are probably best practices that can mitigate some of these problems, and we can use our human understanding of the features to try and reduce risk. Indeed, this is the same problem as 'How do we build a safety critical system with AI, and deterministically show its safe like we could with a simpler one'. Here is a writeup of some of those issues [1]. But not everyone will - because sometimes discrimination is profitable if you can get away with it. If a car insurance company can hide gender discrimination behind a complex model then they are incentivized to do that. So, we possibly have a social problem. The problem is not that newer techniques are 'like a brain and susceptible bias in the same way' because to some extent that isn't true - they work on the real data available unless you feed them movie scripts. The issue is if they intentionally or accidentally become a way of getting around laws society made for good reason. [1] https://intelligence.org/2013/08/25/transparency-in-safety-critical-systems/ https://intelligence.org/2013/08/25/transparency-in-safety-c...