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.
At that time, most heavy-duty calculation took place either in government administration or in astronomical observations. In both of these sites, a great deal of calculation had to be done by hand. Already in the 18th century at the Royal Observatory in Greenwich, forms had been developed that divided the task of a complex calculation into small enough steps — so that, at least at the lower end, the Observatory could employ very cheap schoolboy labor in order to complete them economically.
Over the course of the 19th century, schoolboys were increasingly replaced by women as the cheapest and most reliable form of labor. We can read interesting correspondences from astronomers at Oxford and Harvard recruiting the first generation of women college graduates to perform calculations for half the wages of men. By the late 19th century, the Bureau of Calculation at the Paris Observatory was entirely feminized.
This is a trend that I've noticed in the ~30 years I've been doing this. As computer programming gets more advanced, with greater separation of concerns and finer granularity of tasks, it's becoming more marginalized. My feeling is that programming will no longer be viewed as a lucrative career within the next 10 years, and that it will be largely replaced with algorithms generated by machine learning within 20 years.
I included the quote about calculation being largely done by women before it was automated because we're seeing this happening today in developing countries. They'll catch up to the developed world between 10 and 20 years from now, just in time to also be automated by AI.
I find this all tragic to some degree, because my fondest dream of making computer science available to everyone will also eventually lead to its undoing. This is simply not the future that I anticipated growing up. We're going to end up in a world of ultimate competition and wealth inequality instead of automating the means of production so that everyone can enjoy the prosperity of their basic needs being met with something like UBI. So far the evidence for that is overwhelming, and I'm only seeing the smallest pockets of resistance. So I don't have high hopes for the future anymore.
Great timing: I just finished “Objectivity” the book she co-authored with Peter Gallison. It is a phenomenal work on the history of science, looking at the development of the concept of Objectivity throughout the enlightenment through the lens of scientific atlases. Her writing is beautiful, concrete and lucid. Highly recommended to any Hacker News readers.
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[ 3.0 ms ] story [ 26.5 ms ] threadThere'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/978019...
[2] https://ndpr.nd.edu/news/oliver-wendell-holmes-jr-legal-theo...
[3] https://projecteuclid.org/euclid.ss/1294167961
Over the course of the 19th century, schoolboys were increasingly replaced by women as the cheapest and most reliable form of labor. We can read interesting correspondences from astronomers at Oxford and Harvard recruiting the first generation of women college graduates to perform calculations for half the wages of men. By the late 19th century, the Bureau of Calculation at the Paris Observatory was entirely feminized.
This is a trend that I've noticed in the ~30 years I've been doing this. As computer programming gets more advanced, with greater separation of concerns and finer granularity of tasks, it's becoming more marginalized. My feeling is that programming will no longer be viewed as a lucrative career within the next 10 years, and that it will be largely replaced with algorithms generated by machine learning within 20 years.
I included the quote about calculation being largely done by women before it was automated because we're seeing this happening today in developing countries. They'll catch up to the developed world between 10 and 20 years from now, just in time to also be automated by AI.
I find this all tragic to some degree, because my fondest dream of making computer science available to everyone will also eventually lead to its undoing. This is simply not the future that I anticipated growing up. We're going to end up in a world of ultimate competition and wealth inequality instead of automating the means of production so that everyone can enjoy the prosperity of their basic needs being met with something like UBI. So far the evidence for that is overwhelming, and I'm only seeing the smallest pockets of resistance. So I don't have high hopes for the future anymore.