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People Reduce Workers' Compensation for Using Artificial Intelligence (AI)

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  • Jin Kim
  • Shane Schweitzer
  • Christoph Riedl
  • David De Cremer

Abstract

We investigate whether and why people might reduce compensation for workers who use AI tools. Across 10 studies (N = 3,346), participants consistently lowered compensation for workers who used AI tools. This "AI Penalization" effect was robust across (1) different types of work and worker statuses 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 controlled for. Moreover, the effect emerged not only in hypothetical compensation scenarios (Studies 1-5) but also with real gig workers and real monetary compensation (Study 6). People reduced compensation for workers using AI tools because they believed these workers deserved less credit than those who did not use AI (Studies 3 and 4). This effect weakened when it is less permissible to reduce worker compensation, such as when employment contracts provide stricter constraints (Study 4). Our findings suggest that adoption of AI tools in the workplace may exacerbate inequality among workers, as those protected by structured contracts face less vulnerability to compensation reductions, while those without such protections risk greater financial penalties for using AI.

Suggested Citation

  • Jin Kim & Shane Schweitzer & Christoph Riedl & David De Cremer, 2025. "People Reduce Workers' Compensation for Using Artificial Intelligence (AI)," Papers 2501.13228, arXiv.org.
    • Handle: RePEc:arx:papers:2501.13228
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    References listed on IDEAS

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    2. Adams-Prassl, Abigail & Balgova, Maria & Qian, Matthias, 2020. "Flexible Work Arrangements in Low Wage Jobs: Evidence from Job Vacancy Data," CEPR Discussion Papers 15263, C.E.P.R. Discussion Papers.
    3. Engberg, Erik & Koch, Michael & Lodefalk, Magnus & Schroeder, Sarah, 2023. "Artificial Intelligence, Tasks, Skills and Wages: Worker-Level Evidence from Germany," Working Papers 2023:12, Örebro University, School of Business.
    4. Adams-Prassl, Abi & Balgova, Maria & Qian, Matthias, 2020. "Flexible Work Arrangements in Low Wage Jobs: Evidence from Job Vacancy Data," IZA Discussion Papers 13691, Institute of Labor Economics (IZA).
    5. Kahneman, Daniel & Knetsch, Jack L & Thaler, Richard, 1986. "Fairness as a Constraint on Profit Seeking: Entitlements in the Market," American Economic Review, American Economic Association, vol. 76(4), pages 728-741, September.
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