This article presents an account of job discrimination according to which people redefine merit in a manner congenial to the idiosyncratic credentials of individual applicants from desired groups. In three studies, participants assigned male and female applicants to gender-stereotypical jobs. However, they did not view male and female applicants as having different strengths and weaknesses. Instead, they redefined the criteria for success at the job as requiring the specific credentials that a candidate of the desired gender happened to have. Commitment to hiring criteria prior to disclosure of the applicant's gender eliminated discrimination, suggesting that bias in the construction of hiring criteria plays a causal role in discrimination.

Constructed Criteria. Redefining Merit to Justify Discrimination

Quantifying systematic disparities in numerical quantities such as employment rates and wages between population subgroups provides compelling evidence for the existence of societal biases. However, biases in the text written for members of different subgroups (such as in recommendation letters for male and non-male candidates), though widely reported anecdotally, remain challenging to quantify. In this work, we introduce a novel framework to quantify bias in text caused by the visibility of subgroup membership indicators. We develop a nonparametric estimation and inference procedure to estimate this bias. We then formalize an identification strategy to causally link the estimated bias to the visibility of subgroup membership indicators, provided observations from time periods both before and after an identity-hiding policy change. We identify an application wherein "ground truth" bias can be inferred to evaluate our framework, instead of relying on synthetic or secondary data. Specifically, we apply our framework to quantify biases in the text of peer reviews from a reputed machine learning conference before and after the conference adopted a double-blind reviewing policy. We show evidence of biases in the review ratings that serves as "ground truth", and show that our proposed framework accurately detects these biases from the review text without having access to the review ratings.

Uncovering Latent Biases in Text: Method and Application to Peer Review

Why do women continue to face barriers to success in professions, especially male-dominated ones, despite often outperforming men in similar subjects during schooling? With this study, we draw on r...

Who Gets the Benefit of the Doubt? Performance Evaluations, Medical Errors, and the Production of Gender Inequality in Emergency Medical Education:

Significance Gender inequality has been deemed the “greatest human rights challenge of our time” by the United Nations, and scholars across numerous disciplines agree that gender stereotypes represent a primary way by which this inequality is maintained. Yet changing stereotypes in a systemic, enduring way is extremely difficult. This is at least in part because stereotypes are transmitted and perpetuated through the language societies and organizations use to describe women, especially those in leadership roles. Here, we show that hiring women into leadership positions is associated with organizations characterizing women in more leadership-congruent, agentic ways. This shift mitigates a critical barrier to women’s progression in organizations and society: the incongruence of what it means to be a woman and a leader.

Hiring women into senior leadership positions is associated with a reduction in gender stereotypes in organizational language

Gender-oriented statistical discrimination: Aggregate fertility, economic sector, and earnings among young Chinese workers

Organizations implement formalized procedures to eliminate the biasing effects of gender and other characteristics on evaluations. Prior work shows managers play a key role, but researchers have be...

JoAnne Delfino Wehner

Papers

Inside the Black Box of Organizational Life: The Gendered Language of Performance Assessment: