A new study reveals a surprising bias: workers who admit to using AI—even when their output is identical in quality—are rated as less competent than those who don’t. This phenomenon, known as the AI competence penalty, affects all workers, but it’s far harsher for women, raising concerns about fairness, trust, and gender equality in the future of work.
Researchers from King’s Business School, Peking University, Hong Kong Polytechnic University, and the University of Hong Kong conducted a revealing experiment. Over 1,000 engineers were asked to evaluate a Python code snippet. In every case, the code was identical—the only difference was whether reviewers were told it was written with AI assistance or without it.
The result? Engineers believed to have used AI were rated 9% less competent on average. The quality didn’t matter; the perception did. And for women, this perception gap widened into a significant professional disadvantage.
The same study found that female engineers were penalized 13%, compared to 6% for male engineers. The bias was particularly strong among male reviewers who hadn’t yet adopted AI themselves—these evaluators penalized women 26% more than men for the exact same AI-assisted work.
This suggests that AI tools are not just reshaping productivity—they’re reshaping how credibility is judged, often amplifying pre-existing gender biases in technical fields.
For employees, the competence penalty isn’t just about hurt feelings—it can impact career growth, pay raises, and promotion opportunities. If being transparent about AI usage leads to lower performance ratings, some workers may avoid these tools altogether, trading efficiency for perceived credibility.
The researchers call this a “hidden tax” on AI adoption—a cost that includes lost productivity and the risk of reinforcing systemic inequalities. For organizations, ignoring these biases can undermine innovation, diversity, and trust in the workplace.
The rise of AI should empower workers, not penalize them. Leaders need to create clear policies on AI usage, educate teams about biases, and evaluate performance based on results—not assumptions about tools used. Addressing the AI competence penalty is not only a matter of fairness—it’s essential for building a future where technology serves everyone equally.
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