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Legal Prediction and Calcification Risk
The application of artificial intelligence (AI) to the law has enabled lawyers and judges to predict – with some accuracy – how future courts are likely to rule in new situations. Machine learning algorithms do this by synthesizing historical case law and applying that corpus of precedent to new factual scenarios. Early evidence suggests that these tools are enjoying steady adoption and will continue to proliferate in legal institutions.
Though AI-enabled legal prediction has the potential to significantly augment human legal analyses, it also raises ethical questions that have received scant coverage in the literature. This talk focuses on one such ethical issue: the “calcification problem.” The basic question is as follows: If predictive algorithms rely chiefly on historical case law, and if lawyers and judges depend on these historically-informed predictions to make arguments and write judicial opinions, is there a risk that future law will merely reproduce the past? Put differently, will fewer and fewer cases depart from precedent, even when necessary to achieve legitimate and just outcomes? This is a particular concern for areas of law where societal values change at a rate faster than new precedents are produced. This talk describes the legal, political and ethical dimensions of the calcification problem and suggests interventions to mitigate the risk of calcification.
University of Toronto
Tue, Oct 5, 2021
04:00 PM - 05:30 PM
Centre for Ethics, University of Toronto