Richard A. Berk
Risk assessment algorithms used in criminal justice settings are
often said to introduce “bias”. But such charges can conflate an algorithm’s
performance with bias in the data used to train the algorithm
and with bias in the actions undertaken with an algorithm’s output.
In this paper, algorithms themselves are the focus. Tradeo↵s between
di↵erent kinds of fairness and between fairness and accuracy are illustrated
using an algorithmic application to juvenile justice data. Given
potential bias in training data, can risk assessment algorithms improve
fairness, and if so, with what consequences for accuracy? Although
statisticians and computer scientists can documents the tradeo↵s, they
cannot provide technical solutions that satisfy all fairness and accuracy
objectives. In the end, it falls to stakeholders to do the required
balancing using legal and legislative procedures, just as it always has.
