Stakeholder Guidance to Anticipate and Address Ethical Challenges in Applications of Machine Learning and Artificial Intelligence in Algorithmic Medicine: a Novel Empirical Approach

项目摘要 人工智能应用的潜力，特别是机器学习，以预防、预测和帮助 管理疾病不仅给受影响的个人带来了巨大的希望，也为 人口。这些新的计算策略的特别令人兴奋的例子越来越多地出现在 医疗用深度学习算法的发展。算法已经嵌入到我们的日常生活中， 开始影响人类的决策，从招聘和雇用员工到刑事判决。 在医学之外，对算法可能反映、复制和保持偏见的方式的认识导致了 关于这一主题的理论和实证研究的爆炸性增长。越来越多的人意识到 潜在的算法弱点，包括一些引发对公平性根本问题的担忧， 正义和偏见。预测和解决算法医学中新出现的伦理问题的需要是时候了- 很敏感。随着医疗保健系统越来越多地使用算法来识别、诊断和 治疗方向，算法偏差的后果会产生真实而显著的成本。数不胜数 利益相关者负责开发、应用和解释医学中的算法，以及 然而，受这些学习系统和工具影响最大的利益攸关方很少参与。 这个经验性的和假设驱动的项目的首要目标是阐明道德的图景 在机器的开发、改进和应用方面出现的关切和问题 学习算法医学。首先，我们确定了不同的道德问题和在 机器学习的发展、提炼和应用，通过质疑不同的视角 涉及一系列利益相关者--机器学习研究人员、临床医生、伦理学家和患者。使用 从上半年产生的新见解，我们将进行以证据为基础的信息共享小插曲 一项调查，旨在了解算法的背景对 医生-那些准备在自己的病人护理决策中实施这种创新的人。 最大限度地提高我们在经验伦理调查方面的专业知识记录，这一系列项目 利用对斯坦福大学进行的卓越机器学习研究的访问，包括 NIH资助的调查人员的工作，并提供广泛的、系统地收集的关于伦理问题的数据 在算法的整个开发和实施过程中遇到和预期的。最后， 该项目开发和完善了一份有证据的信息共享调查，用于更好地了解 医生对智能系统的反应。

PROJECT ABSTRACT The potential for artificial intelligence applications, specifically machine learning, to prevent, predict, and help manage disease sparks immense hope not only for the individuals affected, but also for the overall health of populations. Particularly exciting examples of these novel computing strategies are increasingly found in the development of deep learning algorithms for medical use. Already embedded in our daily lives, algorithms have begun to impact human-decision making, from recruitment and hiring of employees to criminal sentencing. Outside of medicine, recognition of the ways algorithms may reflect, reproduce, and perpetuate bias has led to an explosion of theoretical and empirical research on the subject. There is an increasing awareness of potential algorithmic weaknesses, including some that raise concerns about fundamental issues of fairness, justice, and bias. The need to anticipate and address emerging ethical issues in algorithmic medicine is time- sensitive. As health care systems increasingly utilize algorithms for patient identification, diagnosis, and treatment direction, the consequences of algorithmic bias yield real and significant costs. Numerous stakeholders are responsible for the development, application and interpretation of algorithms in medicine, and yet there has been very little engagement of stakeholders most affected by these learning systems and tools. The overarching goal of this empirical and hypothesis driven project is to articulate the landscape of ethical concerns and the issues emerging in the context of the development, refinement, and application of machine learning in algorithmic medicine. First, we determine the distinct ethical issues and problems encountered in the development, refinement, and application of machine learning, by querying the perspectives of a diverse array of stakeholders involved—machine learning researchers, clinicians, ethicists, and patients. Using the new insights generated from the first half, we will conduct an evidence-based, information-sharing vignette survey to understand the impact of the contexts of algorithms on the ethically salient perspectives of physicians—those poised to implement such innovation in their own decision-making for the care of patients. Maximizing our established record of expertise in empirical ethics investigations, this sequence of projects leverages access to the exceptional machine learning research conducted at Stanford University, including work by NIH-funded investigators, and provides extensive, systematically collected data on ethical issues encountered and anticipated throughout the development and implementation of algorithms. Finally, the project develops and refines an evidence-informed information-sharing survey for use in better understanding how physicians react to intelligent systems.