Paper Type
Complete
Abstract
Artificial intelligence systems are increasingly used in employee performance evaluation and related compensation decisions. In some settings, these systems remain opaque to employees, offering ratings without meaningful explanation or effective avenues for challenge. Prior research on algorithmic management and organizational justice has examined opacity and fairness but often treats opacity as a system attribute and fairness as a point-in-time judgment. We develop a conceptual process framework explaining how repeated exposure to opaque AI-mediated performance evaluation erodes procedural justice across recurring evaluation cycles. As employees repeatedly encounter ratings they cannot adequately understand or contest, they shift from uncertainty about individual decisions to broader judgments of procedural unfairness. The framework explains how this erosion shapes downstream outcomes through relational and affective pathways and may, under certain conditions, create pressure for organizational review of evaluation governance. This study contributes a process account of opaque AI evaluation in high-stakes work settings.
Paper Number
1673
Recommended Citation
Mim, Marshia Mostafiz and Taer, Jenna, "Why Black Box AI Performance Reviews Undermine Procedural Justice Over Time" (2026). AMCIS 2026 Proceedings. 12.
https://aisel.aisnet.org/amcis2026/sig_osra/sig_osra/12
Why Black Box AI Performance Reviews Undermine Procedural Justice Over Time
Artificial intelligence systems are increasingly used in employee performance evaluation and related compensation decisions. In some settings, these systems remain opaque to employees, offering ratings without meaningful explanation or effective avenues for challenge. Prior research on algorithmic management and organizational justice has examined opacity and fairness but often treats opacity as a system attribute and fairness as a point-in-time judgment. We develop a conceptual process framework explaining how repeated exposure to opaque AI-mediated performance evaluation erodes procedural justice across recurring evaluation cycles. As employees repeatedly encounter ratings they cannot adequately understand or contest, they shift from uncertainty about individual decisions to broader judgments of procedural unfairness. The framework explains how this erosion shapes downstream outcomes through relational and affective pathways and may, under certain conditions, create pressure for organizational review of evaluation governance. This study contributes a process account of opaque AI evaluation in high-stakes work settings.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
SIG OSRA