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彼此合作，而且还使用自动化技术来做出对其他人的生活产生真实和持久影响的判断。由于隐性偏见分析师之间的潜在相互作用可能会遭受算法和算法偏见，因此对历史上处于弱势群体的公平待遇具有严重的影响，这无意中嵌入了AI系统中。有一个强烈的势在必行的问题，可以通过有效的缓解技术来确保公平的结果，以解决交互式决策支持系统的开放问题。该项目命名为反映正义与公平概念的Aequitas，调查了当代群体公平概念在汇总多个候选人排名的经典任务中，以得出总体公平的共识决定。最终的方法和工具可帮助决策者减轻他们所遭受的隐式偏见，并暴露于无意间嵌入自动化AI排名算法中的算法偏见。这项技术将在从招聘，贷款到教育的领域中具有影响力的应用，在该领域中，委员会经常通过多个决策者的意见做出的决定必须没有偏见的结果。历史上处于弱势群体的人群的公平访问可能会改变生活，贷款和教育资源之类的生活机会，这可能是改变Aequitas项目的社会成果的潜在游戏。此外，通过WPI数据科学REU REU夏季网站和WPI数据科学的跨学科学位课程，将项目活动与对未来的STEM劳动力培训的培训与专注于女性和代表性不足的学生的培训相结合。Aequitas也有望通过提供首次Interachial Interactive AI中的基本AI中的新基础，这也代表着更广泛的影响。预计将获得新的见解，以通过共识建设过程引入对贫困群体的不公平偏见，并在最终排名中表现出来。 作为Aequitas的基础，公平等级的聚合问题是使用约束优化公式对捕获普遍的群体公平标准进行建模的。这种新的公平性优化模型可确保对候选人进行排名的衡量标准，同时仍在根据给定的一组基础排名遵循代表性共识排名。精确而近似偏见的缓解解决方案的家庭设计，可以在各种决策方案中共同保证公平共识的产生。这些新的公平等级聚合服务的量身定制的优化策略可能具有变革性 - 将信封推向了AI的实际道德应用，以进行公平的决策。此外，这些公平的等级聚合方法被整合到精心设计的混合互动系统中，以促进对共识建设过程的理解和信任，并赋予人类决策者参与AI驱动的共识建筑过程以实现无偏见的决策。 Aequitas技术支持比较分析，以可视化单个排名对最终共识结果的影响，并探索汇总和公平标准之间的权衡。用户研究以了解Aequitas系统与人类决策者对公平性的看法相一致的公平性。 此外，研究了Aequitas技术在支持多位分析师协作以达成公平共同决定方面的有效性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准来通过评估来支持的。

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