Paradoxes in Fair Computer-Aided Decision Making
This addresses fairness concerns in high-stakes domains like criminal justice, revealing inherent paradoxes that challenge the implementation of unbiased algorithmic tools.
The paper tackles the problem of fairness in computer-aided decision making, such as in criminal justice, and shows that for non-trivial cases, either the classifier or the human decision-maker must be discriminatory, while also characterizing when fair decision making is possible.
Computer-aided decision making--where a human decision-maker is aided by a computational classifier in making a decision--is becoming increasingly prevalent. For instance, judges in at least nine states make use of algorithmic tools meant to determine "recidivism risk scores" for criminal defendants in sentencing, parole, or bail decisions. A subject of much recent debate is whether such algorithmic tools are "fair" in the sense that they do not discriminate against certain groups (e.g., races) of people. Our main result shows that for "non-trivial" computer-aided decision making, either the classifier must be discriminatory, or a rational decision-maker using the output of the classifier is forced to be discriminatory. We further provide a complete characterization of situations where fair computer-aided decision making is possible.