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应用继续加速， 最终目标是提供信息和改善医疗保健。虽然目前的技术进步很大，但 最近已经引入了关于这些技术的（非预期的）后果的各种关注。为 例如，与数据集偏差相关的问题可以以多种方式表现出来，包括使用非代表性 人口;未得到承认的制度和程序偏见的继续传播;以及公平获得。 考虑到潜在的下游危害，现在必须将伦理、法律的和社会问题（ELSI）与 在生物医学和行为研究以及护理提供中使用数据和人工智能。然而，ELSI的最佳做法 和道德AI（ETAI）尚未完全出现，并且没有记录ELSI/ETAI的标准方法。 在预测模型的开发和使用方面的进展。 基于我们的平台，预测模型指数和交易所储备库（PREMIERE）， 1-2001年R 01补充是开发（Meta）数据，以直接记录和共享ELSI/ETAI相关信息 链接这些信息作为共享预测模型的一部分。为了集中我们的努力，我们解决了日益增长的使用 用于训练和验证机器学习（ML）模型。综合数据集反映了潜在的- 使用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，培训）。生成对抗网络（GANs） 是说明性的。但是，不正确的模拟数据集可能导致算法学习不正确和/或加剧错误。 消除现有的数据集偏差，提出复杂的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道德问题，即通过 关键线人访谈; 2）为AI/ML和ELSI的计算清单建立指导，利用 广泛的利益相关者社区;以及3）作为PREMIERE的一部分，制定和实施该清单， 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，我们建立了一个新的合作加州大学洛杉矶分校和 宾夕法尼亚州立大学（PSU）汇集了人工智能/机器学习，生物医学信息学，法律， 伦理、沟通和医疗保健。我们将共同策划一系列研讨会，召集国家前， 那些已经同意参与这项奋进的人。这些会议的成果将增加 对使用合成数据集及其复杂性的认识;围绕其 用途;以及在预测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.