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The behavior of LLMs in hiring decisions: Systemic biases in candidate selection
Hints of discrimination and lack of principled reasoning in frontier AI systems
Building on this methodology, the present analysis evaluates whether Large Language Models (LLMs) exhibit algorithmic gender bias when tasked with selecting the most qualified candidate for a given job description. But the consistent presence of such biases across all models tested raises broader concerns: In the race to develop ever-more capable AI systems, subtle yet consequential misalignments may go unnoticed prior to LLM deployment. Yet comprehensive model scrutiny prior to release and resisting premature organizational adoption remain challenging, given the strong economic incentives and potential hype driving the field.
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