Standard legal predictive models deliberately include race to maximize accuracy.

Evidence

Machine learning involves using feature frequency information to statistically map correspondence between features and outcomes. For the present purposes, these are case features (arrest charges, arrest report, etc.) and case outcomes (in most instances, the plea bargain reached). Of course, case features also include defendant characteristics, such as age, gender, and race. Thus, if one seeks to build an optimally predictive model, race should be included in the model (see Mayson, 2019; see also Bartlett, Morse, Stanton, & Wallace, 2018). Indeed, this approach, in which bias is a feature, is the usual one when it comes to predictive analytics in the law. — Joseph Avery, Jim Sidanius, Joel Cooper, Gregory Davis, Amanda N., Bias in the Law, 1218

Categorization

• QUESTION: Worth chasing: does including race in a model to predict outcomes encode existing institutional bias rather than neutralize it?
• corroborated: 2 independent passages support this claim — Joseph Avery, Jim Sidanius, Joel Cooper, Gregory Davis, Amanda N., Bias in the Law: A Definitive Look at…, 1218