Abstract
Discrimination law is a possible application of the methods of causal modelling. With it, it brings the possibility of direct statistical evidence on counterfactual questions, something that traditional techniques like multiple regression lack. The kinds of evidence that causal modelling can provide, in large part due to its attention to counterfactuals, is very close to the key question that we ask of jurors in discrimination cases. With this new kind of evidence comes new opportunities. We can better proportion punitive damages to the severity of the discrimination that manifests in a hiring process. We can avoid making certain kinds of assumptions regarding the relationship between protected classes and hiring qualifications that other statistical methods demand from statisticians. We can also distribute restitution to individual claimants in a way that is proportionate to how their application was treated in the hiring process. Here we explore where and how causal modelling can be useful in discrimination law and policy. What elements of law provide friction with this mode of gathering statistical evidence, what new possibilities does it reveal, and how does this integrate with prior judgments regarding statistical evidence?
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Notes
I will leave out discussion of how these values are specifically calculated. See Causal Inference in Statistics by Pearl et al. for an accessible introduction.
Gehring v. Case Corp., 43 F.3d 340 (7th Cir. 1994).
Gehring v. Case Corp., 43 F.3d 340 (7th Cir. 1994).
Adapted from an example in Tribe’s Trials by Mathematics.
Teamsters v. United States, 431 U.S. 324, 97 S. Ct. 1843, 52 L. Ed. 2d 396 (1977).
Sindell v. Abbott Laboratories, 607 P.2d 924, 26 Cal. 3d 588, 163 Cal. Rptr. 132 (1980).
Sindell v. Abbott Laboratories, 607 P.2d 924, 26 Cal. 3d 588, 163 Cal. Rptr. 132 (1980).
Modal realists will object here, but may still agree with the broader point depending on their views on probabilities across sets of possible worlds.
Amazon recently came under scrutiny for such software that discriminated against women by giving lower scores to resumes with women’s organizations on them. See Amazon scraps secret AI recruiting tool that showed bias against women by Jeffrey Dastin.
Obergefell v. Hodges, 135 S. Ct. 2071, 576 U.S. 644, 191 L. Ed. 2d 953 (2015).
Masterpiece Cakeshop v. Colo. Civil Rights, 138 S. Ct. 1719, 584 U.S., 201 L. Ed. 2d 35 (2018).
See Rao’s Three concepts of dignity in constitutional law for many more examples.
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Acknowledgements
I am deeply grateful to Jim Woodward for the invaluable discussion, draft notes, and feedback he has given me. I am also thankful for Sandra Mitchell and Jonathan Fuller for their helpful comments on earlier drafts. The detailed, insightful, and substantive reviews from the anonymous referees were immensely helpful. Their feedback heavily influenced the arguments and trajectory of this paper.
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Shin, J. Statistical evidence, discrimination, and causation. Synthese 200, 490 (2022). https://doi.org/10.1007/s11229-022-03958-7
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DOI: https://doi.org/10.1007/s11229-022-03958-7