A suggestive model removes race to predict race-neutral case outcomes
Evidence
The approach we are suggesting here is distinct, as the goal is to remove suspect features, such as racial bias, from the model, thus bringing its predictions into closer accord with Constitutional mandates for racially equitable treatment of criminal defendants by state actors. Rather than predict likely outcomes, the objective for the model is to suggest how the case would resolve if the defendant were treated race-neutrally. For this reason, we call the model a “suggestive model.” — Joseph Avery, Jim Sidanius, Joel Cooper, Gregory Davis, Amanda N., Bias in the Law, 1219
Categorization
Category: Definitional Content
- DEFINE: Defines the authors’ novel ‘suggestive model’ concept, distinct from standard predictive analytics.
Related
- A bias-removing model should predict counterfactual race-neutral outcomes, not likely ones. (similarity 0.95)
- Predictive models can suggest case outcomes under counterfactual race-neutral processing. (similarity 0.88)
- Standard legal predictive models deliberately include race to maximize accuracy. (similarity 0.85)
- Optimal predictive legal models deliberately include race as a feature (similarity 0.83)
- Prosecutors can benchmark race-neutral decisions using models built from closed case files. (similarity 0.82)
- Closed case files can seed race-neutral predictive models for prosecutorial decisions (similarity 0.78)
- Isolating racial bias requires cases identical except for race (similarity 0.78)