Evaluating Methods for Setting a Prior Probability of Guilt

Abstract

One way of reasoning with uncertainties in the context of law is to use probabilities. However, methods for reasoning about the probability of guilt in a court case requires us to specify a prior probability of guilt, which is the probability of guilt before any evidence is known. There is no accepted approach for specifying the prior probability of guilt but multiple solutions have been proposed. In this paper, we consider three approaches: a prior that is based on the population, a prior based on the number of agents that have similar opportunity as the suspect and a prior that represents a legal norm. For comparing and evaluating the approaches, we use an agent-based model as a ground truth in which all probabilities are known. With the data generated in the ground truth model, we investigate how the choice of prior influences the posterior probability of guilt for both guilty and innocent agents. Using a decision threshold, we can determine the effect of the three approaches on the rates of correct and incorrect convictions and acquittals. We find that the opportunity prior results in higher rates of both correct convictions and false convictions and requires more assumptions and access to data and knowledge than the legal prior and population prior.