CLSC | Socio-Legal Studies Workshop: Ari Ezra Waldman - "Predictive Data Dilemmas"

Abstract
Artificial intelligence (AI) tools are “prediction machines.” Their supposedly impressive capacities to predict recidivism, creditworthiness, honesty, and fraud are critical to their adoption in public and private decision-making. Yet law and technology scholarship approaches AI’s predictions like they are new for law. They aren’t. AI is a window into a problem with which many areas of law have struggled for decades or more, a problem this Article identifies and names: predictive data dilemmas. The law faces predictive data dilemmas whenever it uses information about the past to produce probabilistic knowledge about the future despite the risks of prejudice and bias from doing so. And, it turns out, the law was a prediction machine long before AI. Based on five case studies in which public and private law use different types of historical information to make predictions about the future behavior of individuals or government actors, this Article excavates the law’s approach to predictive data dilemmas across tort law, evidence law, family law, criminal law, bankruptcy law, election law, and the law of standing to reveal lessons for how law can do prediction better, with AI or without it. It provides a taxonomy of the law’s approach to prediction, critiques it, and applies the case studies’ lessons to the AI decision-making context.