FAI: Organizing Crowd Audits to Detect Bias in Machine Learning

机器学习开发团队由于自身的盲点，通常很难检测和减轻有害的刻板印象，特别是当ML系统在全球部署时。使用今天的自动化技术或公平性指标，这些类型的代表性伤害无法轻易量化，并且需要了解特定的社会，文化和历史背景。研究团队将开发一种人群审计服务，利用志愿者和人群工作者的力量来识别机器学习系统中的偏见和不公平的具体情况，将其推广到系统故障，并以开发团队易于操作的形式综合和优先考虑这些发现。研究团队工作的成功将带来识别机器学习系统中偏见和不公平的新方法，从而提高这些系统的信任和可靠性。研究团队的工作将通过一个公共网站分享，这将使记者、政策制定者、研究人员和公众更容易理解算法偏见，并参与发现机器学习系统中的不公平行为。本项目将探讨三个主要的研究问题。第一个是研究从不同人群中招募和激励参与的新技术。第二是为人群工作者开发新的有效的指导形式，以发现偏见的实例并概括偏见的实例。第三是设计新的方法来综合来自人群的发现，以便开发团队能够理解并有效地采取行动。这项研究的成果将包括开发危害分类法;设计和评估新的工具来帮助人群标记、讨论和概括代表性危害;在算法社会学中综合新的设计实践，技术平台，这些平台可以为用户提供机会，通过平台本身识别和报告观察到的不公平系统行为;收集由人群识别的不公平ML系统行为组成的新数据集。这些数据集将支持未来对人群审计系统设计的研究，ML系统中表示危害的性质，以及未来在类似系统上工作的ML团队。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Machine learning development teams often struggle to detect and mitigate harmful stereotypes due to their own blind spots, particularly when ML systems are deployed globally. These kinds of representation harms cannot be easily quantified using today’s automated techniques or fairness metrics, and require knowledge of specific social, cultural, and historical contexts. The researchers team will develop a crowd audit service that harnesses the power of volunteers and crowd workers to identify specific cases of bias and unfairness in machine learning systems, generalize those to systematic failures, and synthesize and prioritize these findings in a form that is readily actionable by development teams. Success in the research team’s work will lead to new ways to identify bias and unfairness in machine learning systems, thus improving trust and reliability in these systems. The research team’s work will be shared through a public web site that will make it easy for journalists, policy makers, researchers, and the public at large to engage in understanding algorithmic bias as well as participating in finding unfair behaviors in machine learning systems. This project will explore three major research questions. The first is investigating new techniques for recruiting and incentivizing participation from a diverse crowd. The second is developing new and effective forms of guidance for crowd workers for finding instances and generalizing instances of bias. The third is designing new ways of synthesizing findings from the crowd so that development teams can understand and productively act on. The outputs of this research will include developing a taxonomy of harms; designing and evaluating new kinds of tools to help the crowd tag, discuss, and generalize representation harms; synthesizing new design practices in algorithmic socio-technical platforms in which these platforms can provide users with the opportunity to identify and report observed unfair system behaviors via the platform itself; and gathering new data sets consisting of unfair ML system behaviors identified by the crowd. These datasets will support future research into the design of crowd auditing systems, the nature of representation harms in ML systems, and for future ML teams working on similar kinds of systems.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.