Achieve Fairness in AI-Assisted Mobile Healthcare Apps through Unsupervised Federated Learning

深度学习模型已部署在越来越多的边缘设备和移动设备中，以提供 医疗保健在我们的生活中，从移动皮肤科助手，移动眼癌(白眼)检测，情感 检测，到全面的生命体征监测。所有这些技术都依赖于 与移动设备一起提供的摄像头，不可避免地会导致不同程度的公平问题，因为 现有人工智能模型中固有的性别、种族和/或社会经济偏见。复合影响因素 包括缺乏来自边缘社区医疗专业人员，关于这些人的信息不足 社区，以及参与数据收集和研究的社会经济障碍。在缺少 反映美国人口的多样性，潜在的安全性或有效性考虑因素可能是 打偏了。更糟糕的是，在数据不足的情况下，人工智能算法可能会误诊代表不足的人， 导致医疗保健差距扩大。因此，迫切需要解决种族、肤色和 人工智能辅助移动诊断中的社会经济不平等。 该项目将解决移动AI助手的公平性问题，使用皮肤病诊断和皮肤 以色彩不平等为研究案例。它不是以集中的方式收集公平的人口数据集，而是 开发联合设备学习框架，以实现参与包容、选择性数据贡献和 持续的个性化。该框架可以在新用户使用时不断学习他们的数据 几乎没有人工监督的移动应用程序。无监督联合学习(FL)框架将是 使用不同的硬件(高端和低端)和型号开发，使来自所有用户的 社会经济地位可以参与这项研究。虽然各种外语技术已经开发出来，但如何 实现硬件异构性和模型异构性的无监督FL尚不清楚。它也是必不可少的 在尽可能少的人工监督下实现这一目标，因为经常有医生是不切实际的 当用户使用这些基于人工智能的应用程序时，给图像贴上标签。此外，即使使用FL，来自 占主导地位的人口仍将主导收集的数据。非统一数据选择技术将是 开发的目的是自动衡量不同数据的重要性，以实现最大的公平性。最后，并非所有人都是神经质的 即使具有相同的偏向数据，网络也表现出相同的固有公平性。一个有公平意识的神经 将开发体系结构搜索框架，以找到能够实现最大公平性的网络。 该项目的预期成果是一个整体框架，通过以下方式减轻不平等的影响 提高对少数群体的推理性能。开发的技术将以移动方式实施 使用不同智能手机的应用程序，并使用公共数据集和UPMC的患者进行评估。数据 代码将提供给公众进行研究。开发的技术可以很容易地扩展到所有 人工智能辅助诊断，并解释了年龄、性别、种族等各方面的不公平。

Deep learning models have been deployed in an increasing number of edge and mobile devices to provide healthcare in our life, from mobile dermatology assistant, mobile eye cancer (leukoria) detection, emotion detection, to comprehensive vital signs monitoring. All these techniques rely on visual assistance of the cameras that come with mobile devices and inevitably lead to different levels of fairness concerns, due to the inherent gender, race and/or socioeconomic bias in existing AI models. Compounding contributing factors include a lack of medical professionals from marginalized communities, inadequate information about those communities, and socioeconomic barriers to participating in data collection and research. In the absence of a diverse population that reflects that of the U.S. population, potential safety or efficacy considerations could be missed. What is worse, with inadequate data, AI algorithms could misdiagnose underrepresented people, leading to increasing health care disparities. Therefore, there is a critical need to address racial, skin color, and socioeconomic inequities in AI-assisted mobile diagnosis. This project will address the fairness issue in mobile AI assistants, using dermatology diagnosis and skin color inequity as the study case. Instead of collecting equitable demographic dataset in a centralized way, it will develop a federated on-device learning framework for participation inclusion, selective data contribution, and continuous personalization. The framework can continuously learn from new users’ data as they use the mobile apps with little human supervision. An unsupervised federated learning (FL) framework will be developed with heterogeneous hardware (high-end and low-end) and models such that users from all socioeconomic status can participate in the research. While various FL techniques have been developed, how to implement unsupervised FL with both hardware and model heterogeneity is not clear. It is also essential to achieve this goal with as little human supervision as possible since it is impractical to have a doctor constantly label the images when users are using these AI-based apps. In addition, even with FL, data from predominating population will still dominate the data collected. Non-uniform data selection techniques will be developed to automatically weigh the importance of different data for maximum fairness. Finally, not all neural networks exhibit the same inherent fairness even with the same biased data. A fairness-aware neural architecture search framework will be developed to find the networks that can achieve the most fairness. The expected outcome of this project is a holistic framework to mitigate the impacts of inequity by improving the inference performance for minorities. The developed techniques will be implemented as mobile apps with heterogeneous smart phones and evaluated with both public dataset and patients at UPMC. Data and code will be made available for public research. The developed techniques can be easily extended to all AI-assisted diagnosis and account for the inequity in various aspects such as age, sex, racial, etc.