Prosecutors can benchmark race-neutral decisions using models built from closed case files.

Evidence

Fortunately, prosecutors’ offices are in possession of information that can be used to form clear and unbiased baselines: closed case files. Employing advanced methods of statistics and computer science, they could use closed case files as a corpus from which to build a model that can produce raceneutral case outcomes. Specifically, they could build a computational model that enables introduction, early in the development of a new criminal case, of an anchor to prosecutorial decision-making. That is, the model would receive the police report and would produce a prediction as to how the case would resolve (based on tens of thousands of previous cases) if the defendant were treated race-neutrally (i.e., all else held equal, the defendant will not receive a different outcome if he is, say, White rather than Black). This prediction could be used by the prosecutor as an anchor; if he or she deviates from the model’s prediction, the reason for the deviation can be described by the prosecutor and incorporated into the model moving forward. — Joseph Avery, Jim Sidanius, Joel Cooper, Gregory Davis, Amanda N., Bias in the Law, 1215

Categorization

• SEED: A proposed bias-correction system that benchmarks actual decisions against a race-neutral prediction model — relevant to the reader’s conviction-vs-measured-bias line as an institutional intervention design.
• XREF: Connects to the implicit-vs-explicit bias gap literature; this is an explicit-measurement-based correction attempt consistent with the reader’s inquiry.
• 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…, 1215