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 人工智能应用程序实现更精细和普遍的测量的潜力， 预测，并提供行为干预为实现精确目标提供了巨大的希望 健康，以维持人口的整体健康。当应用于我们日常生活中遇到的设备时 环境(如个人电脑、移动电话、电脑鼠标，甚至办公家具，如坐立式 办公桌)，机器学习算法可以通过其能力放大技术对健康改善的影响 被动地感知压力，并根据上下文数据和 自我报告的用户反馈。与此同时，这些创新工作的伦理层面--一些 其中涉及对隐私和自主性的根本担忧-需要科学工作者的仔细关注 社区。最关键的是，与终端用户的接触很少，例如 受这些学习系统和工具影响最大的主要利益相关者群体。这项管理工作 补充请求的前提是，范围和目标范围内的目标需求和未满足需求的理由 通过添加以下内容，可以更有效地实现家长赠款的目标(即，不变而是丰富) 与精准健康技术互动的直接接触和亲身体验的参与者 父母拨款中的最后一个利益相关者群体(即患者)。通过将目标1中的患者组扩大到包括 直接参与职业与精准健康交汇点的前沿研究 研究，家长拨款的目的和范围保持不变，而现实世界的应用和 来自母公司赠款的产品的影响大大增强。我们的补充方案 整合了精准的健康技术，涉及使用压力管理的行为干预 进入亲本R01的第一个具体目标。在补充目标1中，我们将使用半结构化访谈 和定性的方法，在发展、完善和 作为精确健康方法学的一部分，机器学习在行为干预中的应用， 特别注意职业健康方面的情况。具体地说，我们的方法引发了广泛的 通过比较两种不同类型的机器学习应用程序(即物理 与数字干预相比)，具有用户可以接受或拒绝的两种不同程度的自主权 人工智能建议的干预措施。这两个申请都提出了关于 ML/AI算法在行为健康研究与实践中的决策作用这项补充质询 该项目利用了对斯坦福大学进行的卓越机器学习研究的访问， 包括由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的数据 作为机器学习应用在精确度、行为和 职业健康。

PROJECT ABSTRACT The potential for artificial intelligence applications to enable more granular and pervasive measurement, prediction, and provide behavioral interventions offers immense promise in reaching the goal of precision health to maintain the overall health of populations. When applied to devices encountered in our everyday environment, (e.g. personal computers, mobile phones, computer mice, even office furniture such as sit-stand desks), machine learning algorithms can amplify the impact of technology on health improvement by its ability to passively sense stress, and to provide just-in-time behavioral interventions based on contextual data and self-reported user feedback. At the same time, the ethical dimensions of these innovative lines of work – some of which entail fundamental concerns about privacy and autonomy – require careful attention from the scientific community. Most critically, there has been little engagement with the end-users of such technologies as a major stakeholder group who are most affected by these learning systems and tools. This administrative supplement request is premised on the fact that the rationale for and unmet needs targeted in the scope and aims of the parent grant can be even more effectively met (i.e. not changed but enriched) by adding participants with direct exposure and personal experience of interacting with precision health technologies to the last stakeholder group in the parent grant (i.e. patients). By extending the patient group in Aim 1 to include those directly participating in cutting-edge research at the intersection of occupational and precision health research, the Aims and Scope of the parent grant remain unchanged, while the real-world application and impact of the products from the parent grant are substantially enhanced. Our Supplemental proposal incorporates precision health technologies involving behavioral interventions of stress management that use ML into the first Specific Aims of the parent R01. In Supplemental Aim 1, we will use semi-structured interviews and qualitative methods to articulate ethical issues in the context of the development, refinement, and application of machine learning in behavioral interventions as part of a precision health methodology, with particular attention to occupational health contexts. Specifically, our methodology elicits a wide range of viewpoints from participants by comparing two distinct types of machine learning applications (i.e. physical versus digital interventions), with two varying degrees of autonomy that users may exercise to accept or reject the AI-recommended interventions. Both of these applications present novel ethical questions regarding the decision-making role of ML/AI algorithms in behavioral health research and practice. This supplementary project 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 as a result of machine learning applications in precision, behavioral, and occupational health.