AI-DCL: EAGER: Human-in-the-Loop Fairness Optimization in Machine Learning with Minimax Loss and an Abstain Option

该项目将实现机器学习算法，探索涉及三个因素的权衡：准确性、公平性和输入数据覆盖。在计算方面，该项目有可能为公平的机器学习算法带来范式转变，同时改善这些算法的不确定性估计。该项目还包括开发“人在循环”优化算法，使人类能够动态调整这三个因素，从而与算法相互作用。在社会学方面，当三个特定因素之间存在可量化的权衡时，这项工作将提供受试者偏好的跨文化研究；它将关注个体之间的行为差异，以回应他们对权衡如何工作的互动探索。本研究的结果将有助于补充先前的相关研究，更定性。这项研究的结果可能对机器学习算法的政策制定产生重大影响。它们还可以提高公众对机器学习算法的认识，并促成更多的人机团队，这在当前时代非常重要，因为机器学习算法越来越多地成为黑盒子，同时在许多关键的现实应用中得到了更广泛的部署。这个项目的目标是研究机器学习算法中准确性、公平性和数据覆盖之间的权衡。该研究团队计划开发具有集成、可优化公平性组件的新型混合人类/机器学习算法。具体目标是开发旨在权衡三个因素的算法：(1)与效用有关的传统平均误差目标，(2)最小化任何训练示例发生的最大误差的最小最大误差目标，这与公平性有关，以及(3)通过提供弃权选项来覆盖算法对输入分布的覆盖，当算法对给出正确答案没有信心时可以利用该选项。该团队将在零和游戏中使用鞍点优化方法开发他们的算法。将设计一个人在环优化算法，让人类动态调整这三个因素，以促进与算法的交互。该团队将使用混合迭代方法进行算法设计和测试，该方法基于广泛应用于人文和社会科学的扎根理论。人类将提供方向（更公平）并指定他们想要覆盖的群体，而实值变化将自动计算。为了从社会学的角度更好地理解效用、公平性和覆盖范围之间的权衡，将在美国的多个社会文化团体以及一个在线平台上进行广泛的跨文化评估，以接触中国和巴西的全球用户。该奖项反映了美国国家科学基金会的法定使命，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。

This project will implement machine learning algorithms that explores tradeoffs involving three factors: accuracy, fairness, and input data coverage. On the computational side, the project has the potential to bring about a paradigm shift for fair machine learning algorithms while improving uncertainty estimates for those algorithms. The project also includes the development of a human-in-the-loop optimization algorithm to enable humans to dynamically tune the three factors and thereby interact with the algorithm. On the sociological side, this work will provide a cross-cultural study of subject preferences when presented with quantifiable tradeoffs between the three specified factors; it will focus on behavioral differences between individuals in response to their interactive explorations of how the tradeoffs work. The results of this study will serve to complement prior related research that is more qualitative. The results of this study could have substantial impacts on policy making on machine learning algorithms. They could also serve to improve the public's in machine learning algorithms and enable more human-machine teams, which is important in the current era where machine learning algorithms have increasingly become black boxes while being more broadly deployed in many crucial real-life applications.The goal of this project is to study the trade-off between accuracy, fairness, and data coverage in machine learning algorithms. The research team plans to develop novel hybrid human/machine-learning algorithms with an integrated, optimizable fairness component. The specific objectives are to develop algorithms that are designed to trade-off between three factors: (1) the traditional average error objective that pertains to utility, (2) a minimax error objective that minimize the maximal error occurring to any training example, which pertains to fairness, and (3) the coverage of the algorithm on the input distribution by providing an abstain option that the algorithm can utilize when it is not confident in giving a correct answer. The team will develop their algorithms using saddle point optimization approaches in zero-sum games. A human-in-the-loop optimization algorithm will be designed for humans to dynamically tune the three factors to facilitate interaction with the algorithm. The team will use a hybrid iterative approach to algorithm design and testing that is based in grounded theory, which is widely used in the human and social sciences. Humans will provide directions (more fairness) and specify the groups they want to cover, while the real-valued changes will be automatically computed. In order to better understand of trade-offs on utility, fairness and coverage from a sociological perspective, a broad cross-cultural evaluation will be performed with multiple social-cultural groups in the United States as well as an online platform to reach global users in China and Brazil.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.