January 30 2020

Machine Learning: art or science? (Part two)

Adrian Cartland

This is the second part of the article/interview. The first part is to be found here

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How do all these developments in statistics and machine learning apply to Law, and what do they mean for law?
The critical question now becomes: what if we are making legal decisions based on machine learning? How can we make decisions that are not explainable?
We should be careful as to whether we are actually using machine learning or whether we are using a regression. Even if a statistical analysis can provide some level of accuracy in predicting the outcome of court cases or the likelihood of a person to reoffend, I have a very negative view of the utility of such system.
Firstly, if you are going to make such a regression you must prove that there is a causal relationship and that the causality runs in the correct way, so that you are not saying that the appearance of umbrellas causes it to rain. A propensity for judges to find a ‘guilty verdict’ in the morning, as opposed to the afternoon, or plaintiffs with a particular name to having a better chance of success, is irrelevant. Similarly, is it that low socio-economic status causes a tendency towards criminality or does causality run the other way?
Secondly, and more importantly, almost all of the variables that are typically used in such an analysis are irrelevant. Picking up pieces of data such as the court name, the general area of law, the solicitor firm name, the date, the jurisdiction are all highly irrelevant the actual question at hand in cases. What matters in a case is the evidence before the judge, the laws that is argued, and analysis of it in application to the facts, at the very minimum.
To be blunt, any system that does not read over the words of the case and come to an understanding of them, and then seek to make a decision based upon those words, must surely be using irrelevant data. That is why, in my view, so called ‘big data’ statistical analyses that purport to predict cases are a mere novelty and are little more than statistical junk. It does not matter whether these are explainable or not because they fall down for another reason altogether.

Article author:
Adrian Cartland

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