Thanks for explaining that perspective. I was seeing things a little too narrow-mindedly for sure. Based on your phrasing of the issue, it seems like we could

Thanks for explaining that perspective. I was seeing things a little too narrow-mindedly for sure. Based on your phrasing of the issue, it seems like we could think of the problem as: can we reduce the number of parameters in an ML model to the point where humans can understand all of the parameters? That's related to an active research area - minimizing model size. ML models can have billions of parameters. It's unfeasible for a human to evaluate all of them. Research shows that (sometimes) you can reduce the number of parameters in a model 97% without hurting it's accuracy [0]. But, 3% of a billion parameters is still way too many for people to evaluate. So I think the answer so far is no, we can't create human-understandable models that perform as effectively as black boxes. [0] https://openreview.net/forum?id=rJl-b3RcF7 https://openreview.net/forum?id=rJl-b3RcF7