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Origins and Effects of Normative Commitments

Final Report Summary - NORMCOMMIT (Origins and Effects of Normative Commitments)

In the previous 5-year period covered by the ERC Consolidator Grant, “Origins and Effects of Normative Commitments,” the PI studied the effects of market interactions and law on normative commitments. This research led to two signature initiatives. The first is the development of a range of datasets in the U.S. legal domain, archival and administrative, on judges and courts where normative commitments can be measured in high-stakes settings. The 12 terabytes of data bridge machine learning, causal inference, and normative theories of justice regarding equal treatment before the law and equality based on recognition of difference. The second is the development of an open-source programming language (oTree) to study normative commitments in experiments, now used in over 23 countries, 10 academic disciplines, private and public sectors, and high schools. Predictive judicial analytics holds the promise of increasing the fairness of law. Much empirical work observes inconsistencies in judicial behavior. By predicting judicial decisions—with more or less accuracy depending on judicial attributes or case characteristics—machine learning offers an approach to detecting when judges most likely to allow extralegal biases to influence their decision making. In particular, low predictive accuracy may identify cases of judicial “indifference,” where case characteristics (interacting with judicial attributes) do no strongly dispose a judge in favor of one or another outcome. In such cases, biases may hold greater sway, implicating the fairness of the legal system.