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Empirical study demonstrating that humans internalize biases from AI systems, continuing to reproduce those biases even after AI assistance is removed—a critical finding for understanding AI-human interaction risks and training data contamination in AI safety.
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This Scientific Reports study investigates how humans adopt AI biases through a series of experiments where participants performed medical diagnostic tasks with and without assistance from a biased AI system. The research found that participants not only made errors when following the AI's biased recommendations, but continued to reproduce those same biases in subsequent tasks performed without AI assistance. This demonstrates that exposure to biased AI systems can cause humans to internalize and perpetuate those biases independently, even after the AI support is removed.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI-Driven Institutional Decision Capture | Risk | 73.0 |
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Humans inherit artificial intelligence biases | Scientific Reports
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Humans inherit artificial intelligence biases
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Subjects
Engineering
Psychology
Abstract
Artificial intelligence recommendations are sometimes erroneous and biased. In our research, we hypothesized that people who perform a (simulated) medical diagnostic task assisted by a biased AI system will reproduce the model's bias in their own decisions, even when they move to a context without AI support. In three experiments, participants completed a medical-themed classification task with or without the help of a biased AI system. The biased recommendations by the AI influenced participants' decisions. Moreover, when those participants, assisted by the AI, moved on to perform the task without assistance, they made the same errors as the AI had made during the previous phase. Thus, participants' responses mimicked AI bias even when the AI was no longer making suggestions. These results provide evidence of human inheritance of AI bias.
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Introduction
Over the last decades, the number of tools based on artificial intelligence (AI) designed to assist human decisions has increased in many professional fields such as justice 1 , 2 , personnel selection 3 , 4 and healthcare 5 , 6 , 7 , 8 . In the medical realm, the advent of AI-based decision support systems has been welcomed as a promise to minimize errors in human decision-making 9 , 10 , 11 , 12
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