FAI: Organizing Crowd Audits to Detect Bias in Machine Learning

机器学习开发团队经常因为他们自己的盲点而努力检测和减少有害的刻板印象，特别是当ML系统部署在全球时。使用今天的自动化技术或公平衡量标准，这些类型的表现危害不容易量化，并且需要特定的社会、文化和历史背景的知识。研究团队将开发一种群体审计服务，利用志愿者和群体工作人员的力量来识别机器学习系统中存在偏见和不公平的特定案例，将这些案例概括为系统性故障，并以开发团队易于操作的形式综合这些发现并确定其优先顺序。研究团队工作的成功将带来识别机器学习系统中的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共享，这将使记者、政策制定者、研究人员和公众更容易参与理解算法偏差，以及参与发现机器学习系统中的不公平行为。这个项目将探索三个主要的研究问题。第一个是研究招募和激励不同人群参与的新技术。第二是为群众性工作者开发新的有效的指导形式，以发现实例和推广偏见实例。第三是设计从人群中综合发现的新方法，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；设计和评估帮助人群标记、讨论和概括表示危害的新型工具；在算法社会技术平台中综合新的设计实践，其中这些平台可以为用户提供机会通过平台本身识别和报告所观察到的不公平系统行为；以及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、ML系统中代表性伤害的性质以及未来ML团队在类似系统上工作的研究。该奖项反映了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。

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.