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

III：小：共识公平决策：交互式偏差缓解技术

• 批准号: 2007932
• 负责人: Elke Rundensteiner
• 金额: $ 50万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007932&HistoricalAwards=false
• 关键词: III; Small; Fair; Decision; Making

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.

随着人工智能在社会技术系统中的使用变得越来越普遍，做出决策的人们不仅经常相互协作，而且还与自动化技术一起做出对其他人的生活产生真正和持久影响的判断。由于分析师可能遭受的隐性偏见和无意中嵌入人工智能系统的算法偏见之间存在潜在的相互作用，这对历史上处于弱势群体的公平和公正待遇产生了严重影响。迫切需要通过有效的偏差缓解技术来解决围绕交互式决策支持系统的开放问题，以确保公平的结果。该项目名为 AEQUITAS，旨在反映正义和公平的概念，研究当代群体公平概念在汇总多个候选人排名以得出总体公平共识决策的经典任务中的应用。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，并暴露自动人工智能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深远的应用，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，必须产生公正的结果。为历史上处于不利地位的人群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（重点关注女性和代表性不足的学生）相结合，也产生了更广泛的影响。通过提供第一个基于交互式共识的偏见缓解解决方案，道德人工智能的根本性新领域。预计将获得新的见解，了解如何通过共识建立过程引入对弱势群体的不公平偏见，并在最终排名中体现出来。 作为 AEQUITAS 的基础，公平排名聚合问题是使用捕获普遍的群体公平标准的约束优化公式来建模的。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，同时仍然根据给定的一组基本排名产生代表性的共识排名。设计了一系列精确和近似偏差缓解解决方案，共同保证在丰富多样的决策场景中形成公平的共识。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的极限。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，以促进对共识构建过程的理解和信任，并使人类决策者能够参与人工智能驱动的共识构建过程，以达成公正的决策。 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标准之间的权衡。进行了用户研究，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的一致程度。 此外，还研究了 AEQUITAS 技术在支持多个分析师合作达成公平的共同决策方面的有效性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

MANI-RANK: Multi-attribute and Intersectional Fairness for Consensus Ranking

MANI-RANK：共识排名的多属性和交叉公平性

• 发表时间: 2022
• 期刊: IEEE International Conference on Data Engineering (ICDE
• 作者: Cachel, K.;Rundensteiner, E.;Harrison, L.
• 通讯作者: Harrison, L.

Help or Hinder? Evaluating the Impact of Fairness Metrics and Algorithms in Visualizations for Consensus Ranking

• DOI: 10.1145/3593013.3594108
• 发表时间: 2023-06
• 期刊: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency
• 作者: Hilson Shrestha;Kathleen Cachel;Mallak Alkhathlan;Elke A. Rundensteiner;Lane Harrison

FairFuse: Interactive Visual Support for Fair Consensus Ranking

• DOI: 10.1109/vis54862.2022.00022
• 发表时间: 2022-07
• 期刊: 2022 IEEE Visualization and Visual Analytics (VIS)
• 作者: Hilson Shrestha;Kathleen Cachel;Mallak Alkhathlan;Elke A. Rundensteiner;Lane Harrison

Fairer Together: Mitigating Disparate Exposure in Kemeny Rank Aggregation

• DOI: 10.1145/3593013.3594085
• 发表时间: 2023-06
• 期刊: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency
• 作者: Kathleen Cachel;Elke A. Rundensteiner

Rank aggregation algorithms for fair consensus

• DOI: 10.14778/3407790.3407855
• 发表时间: 2020-07
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: C. Kuhlman;Elke A. Rundensteiner