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Abstract Despite an increasing reliance on fully-automated algorithmic decision-making in our day-to-day lives, humans still make consequential decisions. While the existing literature focuses on the bias and fairness of algorithmic recommendations, an overlooked question is whether they improve human decisions. We develop a general statistical methodology for experimentally evaluating the causal impacts of algorithmic recommendations on human decisions. We also examine whether algorithmic recommendations improve the fairness of human decisions and derive the optimal decision rules under various settings. We apply the proposed methodology to the first-ever randomized controlled trial that evaluates the pretrial Public Safety Assessment in the United States criminal justice system. Our analysis of the preliminary data shows that providing the PSA to the judge has little overall impact on the judge’s decisions and subsequent arrestee behaviour. 

aihuman is an R package which provides statistical methods for analyzing experimental evaluation of the causal impacts of algorithmic recommendations on human decisions developed by Imai, Jiang, Greiner, Halen, and Shin (2023) . The data used for this paper, and made available here, are interim, based on only half of the observations in the study and (for those observations) only half of the study follow-up period. We use them only to illustrate methods, not to draw substantive conclusions. 

We will never have enough lawyers to serve the civil legal needs of all low and moderate income (LMI) individuals who must navigate civil legal problems. A significant part of the access to justice toolkit must include self-help materials. That much is not new; indeed, the legal aid community has been actively developing pro se guides and forms for decades. But the community has hamstrung its creations in two major ways: first, by focusing these materials almost exclusively on educating LMI individuals about formal law, and second, by considering the task complete once the materials have been made available to self-represented individuals. In particular, modern self-help materials fail to address many psychological and cognitive barriers that prevent LMI individuals from successfully deploying the substance of the materials. In this Article we make two contributions. First, we develop a theory of the obstacles LMI individuals face when attempting to deploy professional legal knowledge.Second, we apply learning from fields as varied as psychology, public health education,artificial intelligence, and marketing to develop a framework for how courts, legal aid organizations, law school clinics, and others might reconceptualize the design and delivery of civil legal materials for unrepresented individuals. We illustrate our framework with examples of reimagined civil legal materials.