Title: Experimental Evaluation of Algorithm-Assisted Human Decision Making
Abstract: Despite an increasing reliance on fully-automated algorithmic decision-making in our day-to-day lives, human beings still make highly consequential decisions. As frequently seen in business, healthcare, and public policy, recommendations produced by algorithms are provided to human decision-makers to guide their decisions. While there exists a fast-growing literature evaluating the bias and fairness of such algorithmic recommendations, an overlooked question is whether they help humans make better decisions. Using the concept of principal stratification, we develop a statistical methodology for experimentally evaluating the causal impacts of algorithmic recommendations on human decisions. We propose the evaluation quantities of interest, identification assumptions, and estimation strategies. We also develop sensitivity analyses to assess the robustness of empirical findings to the potential violation of a key identification assumption. We apply the proposed methodology to preliminary data from the first-ever randomized controlled trial that evaluates the pretrial Public Safety Assessment (PSA) in the criminal justice system.
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