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Machine learning best practice statements have proliferated, but there is alack of consensus on what the standards should be. For fairness standards inparticular, there is little guidance on how fairness might be achieved inpractice. Specifically, fairness in errors (both false negatives and falsepositives) can pose a problem of how to set weights, how to make unavoidabletradeoffs, and how to judge models that present different kinds of errorsacross racial groups. This paper considers the consequences of having higherrates of false positives for one racial group and higher rates of falsenegatives for another racial group. The paper examines how different errors injustice settings can present problems for machine learning applications, thelimits of computation for resolving tradeoffs, and how solutions might have tobe crafted through courageous conversations with leadership, line workers,stakeholders, and impacted communities.

Machine Learning Fairness in Justice Systems: Base Rates, False Positives, and False Negatives