When an AI Judges Your Work: The Hidden Costs of Algorithmic Assessment

TL;DR

This study investigates how workers alter their behavior when aware that AI will evaluate their work, revealing increased output but decreased quality and more external tool usage, with implications for algorithmic assessment impacts.

Contribution

It provides experimental evidence on behavioral changes due to AI-based evaluation, highlighting unintended consequences of algorithmic judgment systems.

Findings

Workers produce more output when evaluated by AI.

Quality of work decreases when judged by AI, regardless of grading method.

Workers use more external tools, including LLMs, under AI evaluation.

Abstract

We use an online experiment with a real work task to study whether workers change their behavior when they know AI will be used to judge their work instead of humans. We find that individuals produce a higher quantity of output when they are assigned an AI evaluator. However, controlling for quantity, the quality of their output is lower, regardless of whether quality is measured using humans or LLM grades. We also find that workers are more likely to use external tools, including LLMs, when they know AI is used to judge their work instead of humans. However, the increase in external tool use does not appear to explain the differences in quantity or quality across treatments.