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Cognitive Bias in Large Language Models: Implications for Research and Practice

L. Zwaan

Abstract

The use of large language models (LLMs) such as ChatGPT in clinical settings is growing, but concerns about their susceptibility to cognitive biases persist. Wang and Redelmeier’s study reveals that LLMs are prone to biases, raising important questions about their role in medical decision-making. To prevent errors in decision-making with LLMs, it is recommended that clinicians aim to critically engage with LLMs (e.g. refuting their hypotheses rather than looking for confirmation) researchers should focus on identifying and evaluating collaborative strategies between AI and human decision-making. Furthermore, research on context-specific implementation is important. We need to ensure that AI complements, rather than replicates, human cognitive processes. (Funded by the Netherlands Organisation for Health Research and Development.)  

DOI: 10.1056/AIe2400961

全文链接：https://ai.nejm.org/doi/abs/10.1056/AIe2400961

大型语言模型中的认知偏差：对研究和实践的影响

L. Zwaan

摘要： 大型语言模型（LLMs），如ChatGPT，在临床设置中的使用正在增长，但人们对它们易受认知偏差影响的担忧持续存在。Wang和Redelmeier的研究表明，LLMs容易受到偏见的影响，这引发了关于它们在医疗决策中角色的重要问题。为了防止在使用LLMs进行决策时出现错误，建议临床医生批判性地与LLMs互动（例如，反驳它们的假设而不是寻找确认），研究人员应专注于识别和评估人工智能与人类决策之间的协作策略。此外，针对特定上下文的实施研究也很重要。我们需要确保人工智能补充而不是复制人类认知过程。（由荷兰卫生研究与发展组织资助。）

NEJM AI, Volume 1 No. 12 December 2024

译文来自于AI工具Kimi