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by rayiner
1839 days ago
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So first of all, you’re incorrect about medical costs being the number one reason for bankruptcies: https://www.washingtonpost.com/politics/2019/08/28/sanderss-... I’ll give you a concrete example in the legal field. Big firms might have reasons to avoid labor-saving automation, because they bill by the hour. But a large fraction of legal work isn’t billed by the hour, it’s contingency work (where the firm gets a certain fraction of a recovery) or fixed fee work. If you’re getting paid 1/3 of the amount you recover (a typical contingency fee) you have enormous incentives to do as little work to get a good result as you can. But those firms don’t use a lot of legal technology either, because it’s just not very good and not very useful. The bulk of legal practice is about dealing with case-specific facts and legal wrinkles. And machine learning tends not to be useful for that, at least in current forms. |
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For example, the idea of using document classification to reduce review costs has been around for a long time. But it took a long time to get any traction. Some of that was about familiarity, but a lot of it was about the original systems being designed to solve the wrong problem. The first products were designed to treat the job as a fairly straightforward binary classification problem. They generally accomplished that task very well. The problem was you had to have a serious case of techie tunnel vision to ever think that legal document classification was just a straightforward binary classification problem in the first place.
Nowadays there are newer versions of the technology that were designed by people with a more intimate understanding of the full business context of large-scale litigation, and consequently are solving a radically reframed version of the problem. They are seeing much more traction.