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Abstract Objectives Traditional police procedural justice theory argues that citizen perceptions of fair treatment by police officers increase police legitimacy, which leads to an increased likelihood of legal compliance. Recently, Nagin and Telep (2017) criticized these causal assumptions, arguing that prior literature has not definitively ruled out reverse causality—that is, legitimacy influences perceptions of fairness and/or compliance influences perceptions of both fairness and legitimacy. The goal of the present paper was to explore this critique using experimental and correlational methodologies within a longitudinal framework. Methods Adolescents completed a vignette-based experiment that manipulated two aspects of officer behavior linked to perceptions of fairness: voice and impartiality. After reading the vignette, participants rated the fairness and legitimacy of the officer within the situation. At three time points prior to the experiment (1, 17, and 31 months), participants completed surveys measuring their global perceptions of police legitimacy and self-reported delinquency. Data were analyzed to assess the extent to which global legitimacy and delinquency predicted responses to the vignette net of experimental manipulations and controls. Results Both experimental manipulations led to higher perceptions of situational procedural justice and officer legitimacy. Prior perceptions of police legitimacy did not predict judgments of situational procedural justice; however, in some cases, prior engagement in delinquency was negatively related to situational procedural justice. Prior perceptions of legitimacy were positively associated with situational perceptions of legitimacy regardless of experimental manipulations. Conclusions This study showed mixed support for the case of reverse causality among police procedural justice, legitimacy, and compliance
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To Impress an Algorithm; Minoritized Applicants’ Perceptions of Fairness in AI Hiring Systems
Technology firms increasingly leverage artificial intelligence (AI) to enhance human decision-making processes in the rapidly evolving talent acquisi- tion landscape. However, the ramifications of these advancements on workforce diversity remain a topic of intense debate. Drawing upon Gilliland’s procedu- ral justice framework, we explore how IT job candidates interpret the fairness of AI-driven recruitment systems. Gilliland’s model posits that an organization’s adherence to specific fairness principles, such as honesty and the opportunity to perform, profoundly shapes candidates’ self-perceptions, their judgments of the recruitment system’s equity, and the overall attractiveness of the organization. Using focus groups and interviews, we interacted with 47 women, Black and Lat- inx or Hispanic undergraduates specializing in computer and information science to discern how gender, race, and ethnicity influence attitudes toward AI in hir- ing. Three procedural justice rules, consistency of administration, job-relatedness, and selection information, emerged as critical in shaping participants’ fairness perceptions. Although discussed less frequently, the propriety of questions held significant resonance for Black and Latinx or Hispanic participants. Our study underscores the critical role of fairness evaluations for organizations, especially those striving to diversify the tech workforce.
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- Award ID(s):
- 1841368
- PAR ID:
- 10541584
- Publisher / Repository:
- Lecture Notes in Computer Science
- Date Published:
- Page Range / eLocation ID:
- 43-61
- Subject(s) / Keyword(s):
- algorithms hiring bias
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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