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14 An Intelligence in Our Image Figure 1 Rate of Enforcement Events per Epoch: Two Subpopulations, Same Crime Rate, Differing Vigilance 0.6 0.5 0.4 0.3 0.2 0.1 More surveilled Less surveilled 0 1 5 10 15 20 Update epoch RAND RR1744-1 we have limited enforcement resources that cannot find and respond to all criminal events. Thus, for a population with a given crime rate, we find violations and enforce the law with a given probability. A vigi- lance parameter determines how much attention we pay in the differ- ent classes and thus the probability of successfully finding and pros- ecuting crimes when they do occur. The system’s vigilance adapts using a mathematically reasonable learning procedure. The system increases vigilance in areas with higher perceived propensity for criminality and/ or in areas with higher enforcement activity based on recorded enforce- ment data.14 The algorithm is reasonably effective but easily leads to inequi- table outcomes. The plot in the figure shows a simulation of its behav- ior giving an increasingly disproportionate frequency of enforcement events in the distinct subpopulations. Such behavior would be accept- 14 The learning law for the algorithm is similar to the ideas behind the broken windows approach to law enforcement advocated by Wilson and Kelling (1982). Enforcement ratePDF Image | Intelligence in Our Image Risks of Bias and Errors in Artificial Intelligence
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