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

利益相关者指导预测和解决机器学习和人工智能在算法医学中的应用中的伦理挑战：一种新颖的经验方法

• 批准号: 10455006
• 负责人: Jane Paik Kim
• 金额: $ 40.29万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-20 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10455006
• 关键词: Address; Adoption; Affect; Agreement; Algorithm Design; Algorithms; Artificial Intelligence; Attention; Attitude; Awareness; Clinical; Clinical Investigator; Complex; Data; Decision Making; Development; Diagnosis; Dimensions; Disclosure; Disease Management; Effectiveness; Empirical Research; Employee; Ensure; Ethical Issues; Ethicists; Ethics; Evaluation; Expert Systems; Explosion; Face; Failure; Familiarity; Funding; Future; Goals; Health; Healthcare Systems; Human; Human Resources; Individual; Interview; Investigation; Judgment; Justice; Knowledge; Learning; Machine Learning; Medical; Medicine; Methodology; Patient Care; Patient-Focused Outcomes; Patients; Perception; Persons; Physicians; Play; Randomized; Research; Research Personnel; Role; Science; Shapes; Societies; Structure; Surveys; System; Time; Training; Translations; Trust; United States National Institutes of Health; Universities; Work; algorithmic bias; artificial intelligence algorithm; clinical decision-making; clinical risk; cost; court; deep learning algorithm; evidence base; experience; improved; innovation; insight; meetings; multidisciplinary; novel; patient population; population health; precision medicine; prevent; recruit; response; stakeholder perspectives; tool

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.

项目摘要 人工智能应用（特别是机器学习）在预防、预测和帮助方面的潜力 控制疾病不仅为受影响的个人，而且为人们的整体健康带来巨大的希望 这些新颖的计算策略的特别令人兴奋的例子越来越多地出现在 用于医疗用途的深度学习算法的开发已经融入到我们的日常生活中。 开始影响人类决策，从招聘和雇用员工到刑事处罚。 在医学之外，对算法反映、再现和延续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呈爆炸式增长，人们对此的认识不断增强。 潜在的算法缺陷，包括一些引起人们对公平性基本问题的担忧， 预测和解决算法医学中新出现的伦理问题的必要性是时间。 随着医疗保健系统越来越多地利用算法进行患者识别、诊断和治疗。 治疗方向、算法偏差的后果会产生大量实际且重大的成本。 利益相关者负责医学算法的开发、应用和解释，以及 然而，受这些学习系统和工具影响最大的利益相关者却很少参与。 这个实证和假设驱动项目的总体目标是阐明伦理的前景 机器的发展、完善和应用背景下出现的关注点和问题 首先，我们确定算法医学中遇到的独特伦理问题和问题。 通过询问不同的观点，机器学习的发展、完善和应用 涉及的利益相关者包括机器学习研究人员、信徒、伦理学家和患者。 上半年产生的新见解，我们将进行基于证据的信息共享小插曲 调查以了解算法背景对道德显着观点的影响 医生——那些准备在自己的患者护理决策中实施此类创新的人。 这一系列项目最大限度地发挥了我们在实证伦理调查方面已建立的专业知识记录 利用斯坦福大学进行的杰出机器学习研究，包括 由 NIH 资助的研究人员开展工作，并提供有关伦理问题的广泛、系统收集的数据 最后， 项目开发并完善了以证据为依据的信息共享调查，以便更好地理解 医生如何对智能系统做出反应。