Researchers studying adverse impact have focused primarily on the statistical properties of various adverse impact tests, almost completely neglecting the human decision-making processes involved in evaluating the fairness of employee selection decisions. The purpose of this study is to (a) use signal detection theory (SDT) to explore the effect of hiring scenario characteristics (i.e., size of the applicant pool, overall selection ratio, and minority proportion of the applicant pool) on laypeople’s sensitivity in detecting adverse impact, (b) explore how several individual differences (i.e., social desirability bias, risk-taking, neuroticism, and ambivalent sexism) may influence their response bias, and (c) replicate my prior research findings surrounding their sensitivity and response bias in making adverse impact judgments (Alexander, 2021). In the current study, 97 working-age adults recruited from an online panel were shown 57 selection scenarios with varying degrees of difference in selection rates between men and women and asked to decide if each scenario was fair or unfair by the Equal Employment Opportunity Commission’s definition of adverse impact. Participants detected adverse impact beyond chance (d′ = 0.45) and exhibited a slightly conservative response bias (c = 0.18). Mixed-effects probit regression analyses were used to estimate SDT metrics reflecting relationships between various hiring scenario characteristics and individual differences in predicting decisions about adverse impact. Cognitive ability, overall selection ratio, and minority proportion of the applicant pool were all positively related to participant sensitivity. Hostile sexism related positively, and benevolent sexism related negatively, to response bias.