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The Limits of Computation in Solving Equity Trade-Offs in Machine Learning and Justice System Risk Assessment
This paper explores how different ideas of racial equity in machine learning, in justice settings in particular, can present trade-offs that are difficult to solve computationally.
Machine Learning Fairness in Justice Systems: Base Rates, False Positives, and False Negatives
Specifically, fairness in errors (both false negatives and false positives) can pose a problem of how to set weights, how to make unavoidable tradeoffs, and how to judge models that present different kinds of errors across racial groups.
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