Great article! She mentions that probability and law are intertwined, in particular in two aspects: degrees of proof, and aleatory contracts. It's also been sug

Great article! She mentions that probability and law are intertwined, in particular in two aspects: degrees of proof, and aleatory contracts. It's also been suggested that the originators of probability were either lawyers or sons of lawyers [1]. There's another link that I've found fascinating: the philosophy of law put forwards by Oliver Wendell Holmes Jr (Supreme Court justice 1902-1932), and the philosophy of modern machine learning. Holmes gave a famous talk in 1897, "The Path of the Law", in which he said that law is nothing more than prediction. Throughout his life he thought and wrote about the relationship between predictions (by lawyers, by their clients, and by judges anticipating review by other judges), rationales (as written out in a judge's decision), and "scientific-style" explanations (the general legal principles that emerge over time). He writes about how the law learns to draw separating lines between clusters of cases, in language that might have come from a modern textbook on k-nearest-neighbours. Reading about Holmes [2], and reading about modern takes on explanation and prediction in machine learning [3], I think there are some striking parallels -- and I wonder if Holmes's ideas might lead to new ways of thinking about explainability in machine learning. [1] https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199607617.001.0001/oxfordhb-9780199607617-e-3 https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/978019... [2] https://ndpr.nd.edu/news/oliver-wendell-holmes-jr-legal-theory-and-judicial-restraint/ https://ndpr.nd.edu/news/oliver-wendell-holmes-jr-legal-theo... [3] https://projecteuclid.org/euclid.ss/1294167961 https://projecteuclid.org/euclid.ss/1294167961