污名、生物标志物与算法偏差：人工智能在精准行为健康领域的建议
Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence.
作者信息
Walsh Colin G, Chaudhry Beenish, Dua Prerna, Goodman Kenneth W, Kaplan Bonnie, Kavuluru Ramakanth, Solomonides Anthony, Subbian Vignesh
机构信息
Biomedical Informatics, Medicine and Psychiatry, Vanderbilt University Medical Center, 2525 West End, Suite 1475, Nashville, TN, USA.
School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, Louisiana, USA.
出版信息
JAMIA Open. 2020 Jan 22;3(1):9-15. doi: 10.1093/jamiaopen/ooz054. eCollection 2020 Apr.
Abstract
Effective implementation of artificial intelligence in behavioral healthcare delivery depends on overcoming challenges that are pronounced in this domain. Self and social stigma contribute to under-reported symptoms, and under-coding worsens ascertainment. Health disparities contribute to algorithmic bias. Lack of reliable biological and clinical markers hinders model development, and model explainability challenges impede trust among users. In this perspective, we describe these challenges and discuss design and implementation recommendations to overcome them in intelligent systems for behavioral and mental health.
摘要
在行为健康护理中有效实施人工智能取决于克服该领域中明显存在的挑战。自我污名和社会污名导致症状报告不足，编码不足则使诊断情况恶化。健康差距导致算法偏差。缺乏可靠的生物学和临床标志物阻碍了模型开发，而模型可解释性方面的挑战则妨碍了用户之间的信任。从这个角度出发，我们描述了这些挑战，并讨论了在行为和心理健康智能系统中克服这些挑战的设计与实施建议。
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