LAUSR

Significance People often rely on artificial intelligence (AI) algorithms to increase their decision-making efficiency and objectivity, yet systemic social biases have been detected in these algorithms’ outputs. We demonstrate that gender bias in a widely used internet search algorithm reflects the degree of gender inequality existing within a society. We then find that exposure to the gender bias patterns in algorithmic outputs leads people to think and act in ways that reinforce societal inequality, suggesting a cycle of bias propagation between society, AI, and users. These findings call for an integrative model of ethical AI that includes human psychological processes to illuminate the formation, operation, and mitigation of algorithmic bias.