Washington Law Review
Harry Surden, Machine Learning and Law, 89 Wash. L. Rev. 87 (2014), available at http://scholar.law.colorado.edu/articles/81.
This Article explores the application of machine learning techniques within the practice of law. Broadly speaking “machine learning” refers to computer algorithms that have the ability to “learn” or improve in performance over time on some task. In general, machine learning algorithms are designed to detect patterns in data and then apply these patterns going forward to new data in order to automate particular tasks. Outside of law, machine learning techniques have been successfully applied to automate tasks that were once thought to necessitate human intelligence — for example language translation, fraud-detection, driving automobiles, facial recognition, and data-mining. If performing well, machine learning algorithms can produce automated results that approximate those that would have been made by a similarly situated person.
This Article begins by explaining some basic principles underlying machine learning methods, in a manner accessible to non-technical audiences. The second part explores a broader puzzle: legal practice is thought to require advanced cognitive abilities, but such higher-order cognition remains outside the capability of current machine-learning technology. This part identifies a core principle: how certain tasks that are normally thought to require human intelligence can sometimes be automated through the use of non-intelligent computational techniques that employ heuristics or proxies (e.g., statistical correlations) capable of producing useful, “intelligent” results. The third part applies this principle to the practice of law, discussing machine-learning automation in the context of certain legal tasks currently performed by attorneys: including predicting the outcomes of legal cases, finding hidden relationships in legal documents and data, electronic discovery, and the automated organization of documents.
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