III: Small: Fair Decision Making by Consensus: Interactive Bias Mitigation Technology

随着人工智能在社会技术系统中的使用变得越来越普遍，做出决策的人不仅经常相互协作，而且还与自动化技术合作，做出对他人生活产生真实和持久影响的判断。这对公平和公平地对待历史上处于不利地位的群体具有严重影响，因为分析师可能遭受的隐性偏差与无意中嵌入人工智能系统的算法偏差之间存在潜在的相互作用。当务之急是用有效的偏见缓解技术解决围绕交互式决策支持系统的公开问题，以确保公平的结果。这个名为AEquitas的项目反映了正义和公平的概念，调查了当代群体公平概念在汇总候选人的多个排名以得出总体公平共识决定这一经典任务中的应用。由此产生的方法和工具有助于决策者减轻他们遭受的隐性偏见，并揭露无意中嵌入到自动AI排名算法中的算法偏见。这项技术将在从招聘、贷款到教育等领域产生影响，在这些领域，通常由委员会在多个决策者的参与下做出的决定必须具有公正的结果。让历史上处于不利地位的群体公平地获得可能改变生活的机会，如工作、贷款和教育资源，这是AEquitas项目潜在的改变社会游戏规则的结果。此外，项目活动与未来STEM劳动力的培训相结合，通过WPI数据科学REU暑期站点和WPI数据科学跨学科学位课程，以女性和代表性不足的学生为重点，也代表了重大的更广泛的影响。AEQUITAS承诺通过提供第一个基于共识的交互式偏见缓解解决方案，在伦理人工智能领域开辟新的天地。预计将获得新的见解，以了解建立共识过程可能引入对弱势群体的不公平偏见的方式，并在最终排名中体现出来。作为AEquitas的基础，公平等级聚集问题采用了一种约束优化公式来建模，该公式捕捉了普遍存在的群体公平准则。这种新的公平保护优化模型确保了对被排名的候选人的公平性度量，同时仍然在给定的一组基本排名之后产生具有代表性的共识排名。设计了一系列精确和近似的偏差缓解解决方案，共同保证在丰富的各种决策场景中产生公平的共识。为这些新的公平排名聚合服务量身定做的优化策略具有潜在的变革性--推动人工智能在公平决策方面的实际伦理应用。此外，这些公平排名聚合方法被整合到精心设计的混合倡议交互系统中，以促进对共识建立过程的理解和信任，并使人类决策者能够参与人工智能驱动的共识建立过程，以达成公正的决定。AEquitas技术支持比较分析，以可视化个别排名对最终共识结果的影响，并探索综合标准的准确性和公平标准之间的权衡。用户研究，以了解AEquitas系统施加的公平与人类决策者对公平的感知有多一致。此外，还研究了AEquitas技术在支持多名分析师协作以达成公平共享决策方面的有效性。这一奖项反映了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。

As the use of AI becomes ever more prevalent in socio-technical systems, people making decisions frequently collaborate not only with each other, but also with automated technologies to make judgements that have real and lasting impact on other people's lives. This has serious implications for the equitable and fair treatment of historically disadvantaged groups, due to the potential interplay between implicit bias analysts may suffer from and algorithmic bias inadvertently embedded in AI systems. There is a strong imperative to address open problems surrounding interactive decision support systems with effective bias mitigation technologies to ensure fair outcomes. This project, named AEQUITAS to reflect the concept of justice and fairness, investigates the application of contemporary notions of group fairness to the classic task of aggregating multiple rankings of candidates to derive an overall fair consensus decision. The resulting methods and tools help decision makers mitigate both the implicit bias they suffer from as well as expose algorithmic bias inadvertently embedded in automated AI ranking algorithms. This technology will have impactful applications in domains from hiring, lending, to education, where decisions often made by committee with input from multiple decision makers must have unbiased outcomes. Fair access for historically disadvantaged groups of people to potentially life changing opportunities such as jobs, loans, and educational resources is a potential game changing societal outcome of the AEQUITAS project. Further, the integration of project activities with the training of a future STEM workforce with focus on female and underrepresented students via the WPI Data Science REU summer site and the interdisciplinary degree programs in Data Science at WPI also represent significant broader impact.AEQUITAS promises to break fundamental new ground in ethical AI by providing the first interactive consensus-based bias mitigation solution. New insights are expected to be gained into the ways in which unfair bias against underprivileged groups may be introduced by a consensus building process and manifest itself in a final ranking. As foundation of AEQUITAS, the fair rank aggregation problem is modeled using a constraint optimization formulation that captures prevalent group fairness criteria. This new fairness-preserving optimization model ensures measures of fairness for the candidates being ranked while still producing a representative consensus ranking following the given set of base rankings. A family of exact and approximate bias mitigation solutions is designed that collectively guarantee fair consensus generation in a rich variety of decision scenarios. Tailored optimization strategies for these new fair rank aggregation services are potentially transformative -- pushing the envelope on practical ethical applications of AI for fair decision making. Further, these fair rank aggregation methods are integrated into carefully designed mixed-initiative interactive systems to facilitate understanding and trust in the consensus building process and to empower human decision makers to engage in an AI-driven consensus building process to reach unbiased decisions. The AEQUITAS technology supports comparative analytics to visualize the impact of individual rankings on the final consensus outcome, as well as to explore the trade-offs between theaccuracy of the aggregation and fairness criteria. User studies to understand how well fairness imposed by the AEQUITAS system aligns with human decision makers' perception of fairness are undertaken. Further, the effectiveness of the AEQUITAS technology in supporting multiple analysts to collaborate towards reaching a fair shared decision is studied.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.