PREMIERE: A PREdictive Model Index and Exchange REpository

项目说明(摘要) 人工智能(AI)在生物医学和行为研究中的使用继续加速， 告知和改善医疗保健的最终目标。虽然到目前为止的技术进步是显著的，但几个 最近，一些人对这种技术的(意想不到的)后果提出了一些担忧。为 例如，与数据集偏差相关的问题可能以多种方式表现出来，包括使用不具代表性的 这些问题包括：对人口的歧视；继续传播未被承认的制度和程序偏见；以及公平获得机会。 鉴于潜在的下游危害，伦理、法律和社会问题(ELSI)现在必须与 数据和人工智能在生物医学和行为研究以及护理提供中的使用。但是，ELSI的最佳实践 和伦理人工智能(ETAI)尚未完全出现，并且没有标准的方式来记录ELSI/ETAI考虑- 在开发和使用预测模型方面的错误。 在我们的平台预测模型索引和交换存储库(Premiere)的基础上，目标是 为期1年的R01补充内容是开发(元)数据，以直接记录和共享有关ELSI/ETAI的信息 将这些信息链接为共享预测模型的一部分。为了集中我们的努力，我们解决了不断增长的使用 用于训练和验证机器学习(ML)模型的合成数据集。合成数据集反映了不充分的- ING真实世界数据集的统计属性，并作为保护私人信息的一种方式进行推广 同时增强总体数据可用性(即，用于培训)。生成性对抗网络(GANS)的使用 是有说服力的。但不正确的模拟数据集可能会导致算法学习不正确和/或精确- ING现有的数据集偏差，提出了复杂的ELSI问题。使用这些问题作为激励用例， 本附录有三个具体目标：1)审查使用合成数据集的伦理，即通过 关键线人访谈；2)为AI/ML和ELSI的计算核对表建立指导，利用 利益相关者的广泛社区；以及3)开发和实施这份清单，作为首映的一部分， 演示如何通过扩展预测模型将ELSI相关信息作为ML模型的一部分进行共享 标记语言(PMML)。为了实现这些目标，我们在加州大学洛杉矶分校和加州大学洛杉矶分校之间建立了新的合作关系 宾夕法尼亚州立大学(PSU)将汇聚人工智能/ML、生物医学信息学、法律、 道德、沟通和医疗保健。我们将共同计划举办一系列研讨会，召集各国前总统 已经同意参与这一努力的PERT。这些会议的成果将会增加。 对合成数据集的使用及其复杂性的认识；围绕其发布的建议 使用；以及在预测性ML模型的背景下记录ELSI/ETAI的方法。

PROJECT DESCRIPTION (ABSTRACT) The use of artificial intelligence (AI) continues to accelerate in biomedical and behavioral research, with the ultimate goals of informing and improving healthcare. While the technical advances thus far are significant, sev- eral concerns have been introduced recently regarding the (unintended) consequences of such techniques. For example, problems related to dataset bias can manifest in multiple ways, including the use of non-representative populations; continued propagation of unrecognized system and process prejudices; and equitable access. Given the potential downstream harm, ethical, legal, and social issues (ELSI) must now be integrated alongside the use of data and AI in biomedical and behavioral research and care delivery. However, best practices for ELSI and ethical AI (ETAI) have yet to fully emerge and there is no standard way of documenting ELSI/ETAI consid- erations in the development and use of predictive models. Building on our platform, the PREdictive Model Index and Exchange REpository (PREMIERE), the goal of this 1-year R01 supplement is to develop (meta)data to document and share information around ELSI/ETAI, directly linking such information as part of a shared predictive model. To focus our efforts, we address the growing use of synthetic datasets to train and validate machine learning (ML) models. Synthetic datasets reflect the underly- ing statistical properties of actual real-world datasets and are promoted as a way of protecting private information while enhancing the overall data availability (i.e., for training). The use of generative adversarial networks (GANs) is illustrative. But improperly simulated datasets can result in an algorithm learning incorrectly and/or exacerbat- ing existing dataset biases, raising complex ELSI questions. Using these questions as a motivating use case, this supplement has three specific aims: 1) to examine the ethics of using synthetic datasets, namely through key informant interviews; 2) to establish guidance for a computational checklist for AI/ML and ELSI, leveraging a broad community of stakeholders; and 3) to develop and implement this checklist as part of PREMIERE, demonstrating how ELSI-related information is shared as part of a ML model by extending the Predictive Model Markup Language (PMML). To achieve these aims, we established a new collaboration between UCLA and Penn State University (PSU) to bring together interdisciplinary experts in AI/ML, biomedical informatics, law, ethics, communication, and healthcare. Together, we will plan a series of workshops that convene national ex- perts who have already agreed to participate in this endeavor. The results of these meetings will be increased awareness around the use of synthetic datasets and their complexities; published recommendations around their use; and methods for documenting ELSI/ETAI in the context of predictive ML models.