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This study evaluates the racial disparities in large language model (LLM) outputs during mortgage underwriting tasks, finding that LLMs recommend more denials and higher interest rates for Black applicants compared to white applicants, even when applications are identical. The research demonstrates that simple prompt engineering can significantly mitigate these disparities, suggesting that LLM behavior can be directed to reduce bias. The findings raise concerns about the integration of AI in financial servi

Author

billatqueensu

Language

EN