Fixing Discrimination in Faculty Hiring Starts With Data

Chad M. Topaz’s critique of the Faculty Merit Act, drafted by the National Association of Scholars, itself embodies another ill of the academy—the conflation of activism with scholarship. Dispassionate readers will quickly grasp that a “co-founder of the Institute for the Quantitative Study of Inclusion, Diversity and Equity” has programmatic goals of his own—the promotion of the illiberal and discriminatory ideology frequently referred to as “Diversity, Equity and Inclusion” (DEI).

That said, Topaz offers an able summary of the limits of statistical analysis within the context of his more ideological goals. His material is a reasonable first draft of a legal brief that a college might file should it be sued for discrimination, if and when the Faculty Merit Act provided strong evidence that faculty and administrators had engaged in substantial discrimination by race, sex and ideology in their faculty hiring and promotion practices. Certainly colleges and universities should be able to avail themselves of Topaz’s services in defending themselves against charges of discrimination.

But it is one thing to say that colleges and universities should be able to defend themselves against charges of discrimination. It is another thing to say that no data may be collected that will allow the public, policymakers, juries and judges to assess whether or not such discrimination has occurred. We have such data for student admissions, and the outcome of Students for Fair Admissions v. Harvard (2023) turned precisely on assessments of that data.

Faculty hiring processes, by contrast, are a black box, precluding any objective assessment that might be used to test for discriminatory policies. The Faculty Merit Act makes such objective assessment possible, allowing for the collection of data that plaintiffs would need to make the case that they have been the victims of discrimination.

That case, presumably, would rest on discriminatory effects beyond what can be explained away by statistical variations. Topaz’s critique silently ignores the possibility that data secured by the Faculty Merit Act would indeed demonstrate discrimination extremely unlikely to have been produced simply as a statistical artifact. Rigorous statistical analysis, indeed, applied to a large body of data, could make clear to any court that we should reject the null hypothesis of nondiscrimination and accept the alternate hypothesis of widespread and severe discrimination by faculty and administrators.

The consequent remedy likely would be reform rather than revolution. Practically speaking, Topaz’s arguments suggest that the Faculty Merit Act would not absolutely prevent academics’ discrimination by race, sex and ideology, but would reduce it to levels that could be explained away as statistical artifacts. This would be an improvement on the status quo that is worth fighting for.