The AI Penalization Effect: People Reduce Compensation for Workers Who Use AI

Jin Kim, Shane Schweitzer, Christoph Riedl and David De Cremer

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Abstract: We investigate whether and why people might adjust compensation for workers who use AI tools. Across 11 studies (N = 3,846), participants consistently lowered compensation for AI-assisted workers compared to those who were unassisted. This "AI Penalization" effect was robust across (1) different types of work (e.g., specific tasks or general work scenarios) and worker statuses (e.g., full-time, part-time, or freelance), (2) different forms of compensation (e.g., required payments or optional bonuses) and their timing, (3) various methods of eliciting compensation (e.g., slider scale, multiple choice, and numeric entry), and (4) conditions where workers' output quality was held constant, subject to varying inferences, or statistically controlled. Moreover, the effect emerged not only in hypothetical compensation scenarios (Studies 1-9) but also with real gig workers and real monetary compensation (Studies 10 and 11). People reduced compensation for workers using AI because they believed these workers deserved less credit than those who did not use AI (Studies 7 and 8). This mediated effect attenuated when it was less permissible to reduce worker compensation, such as when employment contracts provide stricter constraints (Study 8). Our findings suggest that adoption of AI tools in the workplace may exacerbate inequality among workers, as those protected by structured contracts are less vulnerable to compensation reductions, while those without such protections are at greater risk of financial penalties for using AI.

Date: 2025-01, Revised 2025-05
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