Who’s the Fairest of Them All: An Experiment Testing How Source, Outcome, and Process Description Affect Perceived Fairness in Hiring Decisions

Complex technologies are now a ubiquitous part of hiring processes. This experiment tests how potential job applicants perceive hiring decisions based on outcome (acceptance or rejection), decision source (algorithm, hiring manager, hiring manager using algorithmic data), and description (none, definitional, or debiasing) with 246 working-age adults in the United States. Drawing on hiring literature and the machine heuristic, we pose hypotheses about how variations in decision outcome, source, and description will be received by participants. Results confirm that rejections are seen as less fair, objective, and capable of accounting for participant uniqueness than job acceptances. Further, for hiring managers and hiring managers using algorithmic data, providing no description of the process is seen as less fair than offering definitional descriptions, and descriptions of debiasing lead to the highest evaluations. However, a boomerang effect emerged for algorithms accompanied by a debiasing statement for both fairness and objectivity. Theoretical and practical implications for the machine heuristic and fairness in personnel selection are presented.