FAI: Identifying, Measuring, and Mitigating Fairness Issues in AI

人工智能中的偏见和歧视越来越受到人们的关注。不幸的是，公平人工智能的积极概念很难定义。例如，很难区分(想要的)个性化和(不想要的)偏见。这些差异通常取决于背景，例如在进行医疗诊断时使用性别或种族，而在确定保险是否应涵盖医疗程序时使用相同的属性。这一点尤其困难，因为人工智能系统被用于新的背景下，使以前从未见过的、尚未建立社会公平概念的产品和服务成为可能。这个多学科项目将构建一个在社会背景下理解公平的框架和分类法。将开发人机交互方法，以学习基于人与人工智能系统交互的公平感。将开发自动化方法，将这些感知与框架联系起来，使开发人员(最终是自动化人工智能系统)能够响应并纠正系统用户所感知的问题。这一探索性项目将开发一个包含亚里士多德式公平(分配正义与矫正正义)和罗尔斯公平(权利和机会平等)概念的分类法。一个正式的文献调查将被用来建立一个公平的社会背景以及它们如何与分类学相关的框架。对模型的孤立感知和对比感知的实验将被用来评估人们认为人工智能系统公平或不公平的情况。将开发工具，根据人们对该制度的看法和社会背景，从分类的角度确定和解释公平问题。虽然超出了这个项目的范围，但这些工具的结果可能会被用来自动调整人工智能系统，以减少不公平。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Bias and Discrimination in Artificial Intelligence (AI) has been receiving increasing attention. Unfortunately, the positive concept Fair AI is difficult to define. For example, it is hard to distinguish between (desired) personalization and (undesired) bias. These differences often depend on context, such as the use of gender or ethnicity in making a medical diagnosis vs. using the same attributes in determining if insurance should cover a medical procedure. This is particularly difficult as AI systems are used in new contexts, enabling products and services that have not been seen before and for which societal concepts of fairness are not yet established. This multidisciplinary project will construct a framework and taxonomy for understanding fairness in societal contexts. Human-computer interaction methods will be developed to learn perceptions of fairness based on human interaction with AI systems. Automated methods will be developed to relate these perceptions to the framework, enabling developers (and eventually automated AI systems) to respond to and correct issues perceived by users of the systems.This exploratory project will develop a taxonomy incorporating concepts of Aristotelian fairness (distributive vs. corrective justice) and Rawlsian fairness (equality of rights and opportunities). A formal literature survey will be used to establish a framework for societal contexts of fairness and how they relate to the Taxonomy. Experiments with perceptions of models both in isolation and in comparison will be used to evaluate situations where people perceive AI systems as fair or unfair. Tools will be developed to identify and explain fairness issues in terms of the taxonomy, based on the elicited perceptions and societal context of the system. While beyond the scope of this project, the outcome of these tools could potentially be used to automatically adjust AI systems to reduce unfairness.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.