This is a neat summary of questions and issues around the explicability of algorithms, in the form of an account of a recent academic conference. The author sums up his own contribution to the debate pithily and slightly alarmingly:
Modern machine learning: We train the wrong models on the wrong data to solve the wrong problems & feed the results into the wrong software
There is a positive conclusion that there is growing recognition of the need to study the social impacts of machine learning – which is clearly essential from a public policy perspective – but with concern expressed that multidisciplinary research in this area lacks a clear home.
Zachary Lipton – Approximately Correct