The doctor will polygraph you now: ethical concerns with AI for fact-checking patients.

ArXiv Pub Date : 2024-11-11
James Anibal, Jasmine Gunkel, Shaheen Awan, Hannah Huth, Hang Nguyen, Tram Le, Jean-Christophe Bélisle-Pipon, Micah Boyer, Lindsey Hazen, Yael Bensoussan, David Clifton, Bradford Wood
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Abstract

Artificial intelligence (AI) methods have been proposed for the prediction of social behaviors which could be reasonably understood from patient-reported information. This raises novel ethical concerns about respect, privacy, and control over patient data. Ethical concerns surrounding clinical AI systems for social behavior verification can be divided into two main categories: (1) the potential for inaccuracies/biases within such systems, and (2) the impact on trust in patient-provider relationships with the introduction of automated AI systems for "fact-checking", particularly in cases where the data/models may contradict the patient. Additionally, this report simulated the misuse of a verification system using patient voice samples and identified a potential LLM bias against patient-reported information in favor of multi-dimensional data and the outputs of other AI methods (i.e., "AI self-trust"). Finally, recommendations were presented for mitigating the risk that AI verification methods will cause harm to patients or undermine the purpose of the healthcare system.

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医生现在要对你进行测谎:人工智能对病人进行事实核查的伦理问题。
临床人工智能(AI)方法已被提出,用于预测可从患者报告数据中合理理解的社会行为。这引发了有关尊重、隐私和患者对如何使用其健康数据的认识/控制的伦理问题。围绕用于社会行为验证的临床人工智能系统的伦理问题主要分为三类:(1) 在未获得知情同意的情况下为特定的验证任务回顾性地使用患者数据;(2) 此类系统中可能存在的不准确性或偏差;(3) 引入用于事实核查的自动化人工智能系统对患者与医疗服务提供者之间信任关系的影响。此外,该报告还展示了模拟滥用核查系统的情况,并确定了潜在的 LLM 偏差,即不利于患者报告的信息,而有利于多模态数据、发表的文献和其他人工智能方法的输出(即人工智能自我信任)。最后,提出了降低人工智能验证系统对患者造成伤害或破坏医疗保健系统宗旨的风险的建议。
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