Use Explainable AI to Improve the Trust of and Detect the Bias of AI Models

项目摘要/摘要 AD/ADRD是一种日益严重的全国性公共卫生危机，因为美国≥65岁的人数 预计到2050年将翻一番。我们的父母基金是用来测量心肺功能的 体能(CRF)作为体力活动的生物标志物在年最广泛的流行病学研究中 近100万退伍军人使用退伍军人管理局的世界级电子健康记录和先进的人工智能 智能技术。家长赠款的目的是(1)确定以下关系 CRF和事件AD/ADRD，考虑到非线性关系和潜力 CRF与其他危险因素的相互作用以及(2)定义与CRF相关的增量CRF水平 以逐步降低AD/ADRD的风险，总体上，以及按年龄、性别和种族分组。我们的目标 3是开发和验证基于深度学习的风险预测模型，以确定最优的风险预测模型 CRF水平有利于个体达到AD/ADRD的最低风险。深度学习是人工智能的关键 智能(AI)技术。人工智能在过去十年中取得了长足的进步。然而，人工智能 模特通常被视为“黑匣子”，因为她们很难解释。理解什么是人工智能 模型是合乎道德地使用人工智能的先决条件，因为利益相关者不能信任模型或 检测与开发相关的潜在的有意和无意的偏见，或 在不了解模型的情况下使用模型。我们相信，可解释的人工智能是一个强大的工具 以解决信任和偏见的生物伦理问题。可解释人工智能的目的是让它 使人类用户能够理解和信任由 AI模型，并对其进行调试和提炼。具体地说，这份补充将测试AI的效果 在模拟环境中通过招募一组 利益攸关方，并使用基于情景的方法。拟议中的潜在广泛影响 工作是，它将推动人工智能/ML在生物医学和行为学中的伦理开发和使用 使用可解释的人工智能方法的科学。

Project Summary/Abstract AD/ADRD is a growing national public health crisis as the number of Americans ≥65 years is projected to double by 2050. Our parent grant was designed to measure cardiorespiratory fitness (CRF) as a biomarker of physical activity in the most extensive epidemiological study in nearly 1 million Veterans using VA’s world-class electronic health record and advanced artificial intelligence technologies. The parent grant aims are (1) to determine the relationship between CRF and incident AD/ADRD, taking into consideration a non-linear relationship and potential interactions of CRF with other risk factors and (2) to define incremental CRF levels that are linked to progressively lower risk of AD/ADRD, overall, and in subgroups by age, sex, and race. Our Aim 3 is to develop and validate a deep learning-based risk prediction model to determine the optimal CRF level for individuals to achieve the lowest risk of AD/ADRD. Deep learning is a key Artificial intelligence (AI) technique. AI has demonstrated great strides in the past decade. However, AI models are often viewed as “black box” as they are difficult to explain. Understanding what an AI model does is a prerequisite to the ethical use of AI, because stakeholders can’t trust a model or detect the potentially intended and unintended biases associated with the development or utilization of the model without understanding it. We believe that explainable AI is a powerful tool to address the bioethics issues of trust and bias. The purpose of explainable AI is to make it possible for human users to understand and trust the decisions or recommendations offered by the AI model, and to debug and refine it. Specifically, this supplement will test the effect of AI model explanation on trust and bias detection in a simulated environment by recruiting a set of stakeholders and using a scenario-based approach. The potential broad impact of the proposed work is that it will advance the ethical development and use of AI/ML in biomedical and behavioral sciences using explainable AI methods.