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越来越普遍，人们不仅经常相互合作，而且还与自动化技术合作，以做出对他人生活产生真实的持久影响的判断。这对公平和公正地对待历史上处于不利地位的群体产生了严重影响，因为分析师可能遭受的隐性偏见与人工智能系统中无意中嵌入的算法偏见之间存在潜在的相互作用。有一个强烈的必要性，以解决开放的问题，围绕互动决策支持系统与有效的偏见缓解技术，以确保公平的结果。这个项目，命名为AEQUITAS，以反映正义和公平的概念，研究应用当代群体公平的概念，以经典的任务，汇总多个排名的候选人，以获得一个整体公平的共识决定。由此产生的方法和工具可以帮助决策者减轻他们所遭受的隐性偏见，并暴露自动AI排名算法中无意中嵌入的算法偏见。这项技术将在招聘、贷款和教育等领域产生影响，这些领域的决策通常由委员会根据多个决策者的意见做出，必须有公正的结果。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，是AEQUITAS项目的一个潜在的改变游戏规则的社会成果。此外，通过WPI数据科学REU夏季网站和WPI数据科学跨学科学位课程，将项目活动与未来STEM劳动力培训相结合，重点关注女性和代表性不足的学生，这也代表了更广泛的影响。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AI方面开辟新的基础。预计将获得新的见解，以了解对弱势群体的不公平偏见可能会通过建立共识的过程引入，并在最终排名中表现出来。 作为AEQUITAS的基础，公平排名聚合问题建模使用约束优化配方，捕捉流行的组公平性标准。这种新的保持公平性的优化模型确保了被排名的候选人的公平性，同时仍然在给定的一组基础排名之后产生代表性的共识排名。一个家庭的精确和近似的偏差缓解解决方案的设计，共同保证公平的共识生成在丰富的各种决策方案。为这些新的公平排名聚合服务量身定制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道德应用。此外，这些公平排名聚合方法被集成到精心设计的混合主动交互系统中，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，以达成无偏见的决策。AEQUITAS技术支持比较分析，以可视化个人排名对最终共识结果的影响，并探索聚合准确性和公平性标准之间的权衡。进行用户研究，以了解AEQUITAS系统所施加的公平性与人类决策者对公平性的看法是否一致。 此外，AEQUITAS技术的有效性，支持多个分析师进行合作，以达成一个公平的共享decisions.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.