Bridging the AI Chasm: Can EBM Address Representation and Fairness in Clinical Machine Learning?
Bridging the AI Chasm: Can EBM Address Representation and Fairness in Clinical Machine Learning?
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DOI：
10.1080/15265161.2022.2055212
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发表时间：
2022-05
影响因子：
13.4
通讯作者：
Cho, Mildred K.
中科院分区：
文献类型：
--
作者：
Martinez-Martin, Nicole;Cho, Mildred K.
尽管30篇公开的同行评论混淆了偏见，随机化可以控制研究人群中的选择偏见，但这些方法没有解决嵌入模型本身的更大的代表性问题，特别是因为潜在数据中固有的社会不平等。临床研究的规范没有成功地解决更广泛的公平问题，因此不会解决算法评估的这些问题。辩称，计算机科学和临床研究的认识论和伦理文化之间的冲突是人工智能鸿沟的原因。他们将计算机科学和临床研究之间的认知鸿沟归结为这些学科之间的方法差异。它们反映了人工智能/机器学习(ML)的数据驱动文化与保护参与者免受剥削的研究伦理目的之间的中心紧张关系，特别是在同意和数据保护方面。根据McCradden等人的说法，在临床环境中实施ML所产生的主要伦理挑战是潜在的偏离护理标准，偏见是患者的风险来源。因此，为了减少这些风险，他们主张使用临床研究规范和方法对医学ML进行严格的评估。他们的方法侧重于随机化和前瞻性研究设计，以控制威胁AI/ML有效转化为临床应用的偏见。然而，在将认知和伦理冲突平坦化为相互冲突的方法问题时，这种方法忽略了关于如何定义和解决偏见的关键问题。
30 OPEN PEER COMMENTARIES confounding bias, and randomization can control for selection bias within the study population, these methods do not address larger problems of representation embedded in the models themselves, especially because of social inequalities inherent in the underlying data. The norms of clinical research have not successfully addressed the broader issues of fairness, and thus will not solve those problems for algorithmic evaluation.McCradden et al. argue that a clash between the epistemic and ethical cultures of computer science and clinical research accounts for the AI chasm. They describe the epistemic divide between computer science and clinical research as coming down to a difference in methods between these disciplines. They characterize a central tension between the data-driven culture of AI/Machine Learning (ML) and the purpose of research ethics to protect participants from exploitation, particularly in terms of consent and data protection. According to McCradden et al., the main ethical challenge arising from implementation of ML in a clinical context is the potential deviation from standard of care, with bias presenting a source of risk to patients. In order to mitigate these risks, they therefore advocate for rigorous evaluation of medical ML using clinical research norms and methods. Their approach focuses on randomization and prospective study designs to control for biases that threaten the effective translation of AI/ML to clinical applications. However, in flattening the epistemic and ethical clash to a matter of conflicting methods, this approach ignores key issues regarding how bias is defined and addressed.
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影响因子：
2.1
作者：
Kostick-Quenet, Kristin M.;Cohen, I. Glenn;Blumenthal-Barby, J. S.
通讯作者：
Blumenthal-Barby, J. S.
影响因子：
13.4
作者：
McCradden, Melissa D.;Anderson, James A.;Shaul, Randi Zlotnik
通讯作者：
Shaul, Randi Zlotnik
影响因子：
2.4
作者：
Kauh TJ;Read JG;Scheitler AJ
通讯作者：
Scheitler AJ
影响因子：
56.9
作者：
Obermeyer, Ziad;Powers, Brian;Mullainathan, Sendhil
通讯作者：
Mullainathan, Sendhil
影响因子：
39
作者：
Gianfrancesco MA;Tamang S;Yazdany J;Schmajuk G
通讯作者：
Schmajuk G