Disparate Impact

A theory of employment discrimination holding that a facially neutral policy or practice unlawfully discriminates if it produces a statistically significant adverse effect on a protected group, without adequate business justification. Disparate impact claims depend heavily on statistical evidence, making expert testimony central to both liability and damages analysis. Funders assess the quality of available workforce data, the strength of the statistical methodology, and whether the employer has a viable business necessity defense.

Disparate impact underwriting turns almost entirely on the quality of the plaintiff's workforce data and statistical methodology, so funders commission or require an independent statistical review before treating a claimed adverse effect as litigation-ready — a marginal or fragile regression is a common reason a facially compelling fact pattern gets declined. Funders also assess the strength of an employer's likely business-necessity defense in parallel, since a policy with a well-documented job-relatedness rationale can defeat an otherwise statistically sound impact claim.