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It's not possible to reduce all lawsuits to compact formulae. Part of the legal profession is in interpreting old laws in the modern context, for instance, whic
by MaxBarraclough 6y ago
It's not possible to reduce all lawsuits to compact formulae. Part of the legal profession is in interpreting old laws in the modern context, for instance, which would take something close to a general AI. More broadly, reasoning about legal edge-cases needs a sophisticated understanding of the law and of the world. Also, jurors can't be replaced by software, as they must be 'peers' by definition.
I imagine it would be much easier to build a system to roughly estimate the odds of a lawsuit being successful, inputting salient features of the case and running the numbers, without deep reasoning about the particulars. I don't know if work is already being done on this but I wouldn't be surprised. A lot of money must ride on knowing which corporate law battles can be won.
- sjy 6y agoWork is being done on this – here’s some in the human rights context: https://link.springer.com/article/10.1007/s10506-019-09255-y https://link.springer.com/article/10.1007/s10506-019-09255-y But the outcome of high-stakes commercial litigation is much more complex than “win or lose.” Most cases settle, so the ones that result in published legal judgments which can be fed into a machine learning tool are an unrepresentative minority. Participating in this kind of litigation also costs millions and attracts public attention, leading to innumerable second-order effects (eg. public relations damage, law reform) that are hard to predict and may be more significant than whatever specific legal decision a judge makes. So being able to put a numerical probability on the legal opinion “we think the company has a good chance of winning this case” may not be that helpful.