The AI Penalty: People Reduce Compensation for Workers Who Use AI

TL;DR

This research demonstrates that people generally reduce compensation for workers using AI, driven by perceptions of effort and agency, but strategic control and contractual safeguards can mitigate this bias.

Contribution

The study provides robust evidence of an 'AI penalty' in compensation decisions and identifies factors that influence its magnitude and potential mitigation.

Findings

Participants lower compensation for AI-using workers across scenarios.

Perceived effort and agency influence compensation reductions.

Strategic control and contractual protections can reduce the AI penalty.

Abstract

We investigate whether and why people might adjust compensation for workers who use AI tools. Across 13 studies (N = 4,956), participants consistently lowered compensation for workers who used AI compared to those who did not. This "AI penalty" is robust across different work scenarios and work tasks, worker statuses, forms and timing of compensation, methods of eliciting compensation, and perceptions of output quality. Moreover, the effect emerges in both hypothetical compensation scenarios as well as real monetary compensation of gig workers. We find that perceived effort and perceived agency -- the degree to which an individual serves as the originating source of the core intellectual or creative contribution in a task -- explain decisions to reduce compensation for AI-users. However, the penalty is not inevitable. Workers who strategically retain creative agency over core tasks recover most of the AI penalty, and employment contracts that make compensation reductions impermissible provide structural means of reducing the AI penalty.