LLM Decision-Making Biases: A Serious Issue
Large Language Models (LLMs) are increasingly used in sensitive domains like hiring, healthcare, and law, but their inherent biases in decision-making processes are a serious concern. Research reveals that LLM outputs are susceptible to prompt engineering, question phrasing, and label design, exhibiting cognitive biases similar to humans, such as positional bias, framing effects, and anchoring bias. The article uses experimental data to demonstrate these biases and proposes mitigation strategies, including neutralizing labels, varying order, validating prompts, optimizing scoring mechanics, adopting more robust ranking methodologies, designing and stress-testing classification schemas, strategically vetting and diversifying model portfolios, using temperature and repetitions to address variance, not systematic bias, critically evaluating human baselines, and approaching consensus/ensembles with caution. Ultimately, the article emphasizes the crucial need to understand and mitigate LLM biases in high-stakes applications to ensure fair and reliable decisions.
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