CAREER: Fair Artificial Intelligence for Intelligent Humans: Removing the Barriers to Deployment of Fair AI Technologies

职业：智能人类的公平人工智能：消除公平人工智能技术部署的障碍

• 批准号: 2046381
• 负责人: James Foulds
• 金额: $ 54.67万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046381&HistoricalAwards=false
• 关键词: CAREER; Fair; Artificial; Intelligence; Intelligent

There is growing awareness that artificial intelligence (AI) and machine learning systems can in some cases behave in unfair and discriminatory ways with harmful consequences in many areas including criminal justice, hiring, medicine, and college admissions. Techniques for ensuring AI fairness have received a lot of attention in the AI literature. However, these techniques are yet to see a substantial degree of deployment in real systems, which has thus-far limited their real-world impact. This is likely due in part to several practical challenges for deploying fair AI technologies. Firstly, the conventional wisdom is that fairness brings a cost in prediction performance which could affect an organization's bottom-line. Secondly, it is difficult to know which mathematical definition of AI fairness is appropriate to adopt since the definitions conflict with each other and encode different value systems. Finally, there is a chicken-and-egg problem, in that public pressure for an organization to adopt fairness considerations into an AI system only increases after this has been successfully demonstrated elsewhere. This research will develop technical solutions to resolve these human-facing barriers for the adoption of AI fairness techniques, thereby increasing deployment and the subsequent positive real-world impact.To resolve the practical limitations of fair AI techniques, this research incorporates human-centered considerations into the design and execution of fair AI algorithms, connecting and advancing the state of the art in statistical machine learning, fair AI, and human-centered AI. The first track of the project will develop methods for obtaining “fairness for free,” in which the fairest possible solution is found when sacrificing little-to-no performance. The researchers will design black-box, gray-box, and white-box approaches to this task. Then, the second track of the research will focus on developing explainable AI and data visualization techniques to help humans assess and trade off the consequences of different competing notions of fairness. A key step to accomplish this is to create a unifying fairness framework which systematically encodes the space of possible fairness metrics. Finally, in the third track of the project, the researchers will develop practical solutions to several real-world applications of AI fairness, including the allocation of medical resources, and AI-based career counseling. The solutions will involve both applied and fundamental AI research, and will facilitate the evaluation of the methods developed in the first two tracks. The project also includes initiatives for outreach, broadening participation in science, technology, engineering, and mathematics (STEM) fields, training and educating graduate students, and curriculum development.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.

人们越来越意识到人工智能（AI）和机器学习系统在某些情况下可以以不公平和歧视性的方式行事，在许多领域都有有害后果，包括刑事司法，招聘，医学和大学招生。确保AI公平性的技术在AI文献中引起了很多关注。但是，这些技术尚未在实际系统中看到很大程度的部署，从而限制了其现实世界的影响。这可能部分归因于部署公平AI技术的几个实际挑战。首先，传统的观点是，公平性带来了预测绩效的成本，这可能会影响组织的底线。其次，由于定义相互冲突并编码不同的值系统，因此很难知道AI公平性的数学定义适合采用。最后，存在一个鸡肉和蛋的问题，因为公众将组织采用公平考虑到AI系统的压力只有在此之后才能在其他地方成功证明。 This research will develop technical solutions to resolve these human-facing barriers for the adoption of AI fairness techniques, thereby increasing deployment and the subsequent positive real-world impact.To resolve the practical limitations of fair AI techniques, this research incorporates human-centered considerations into the design and execution of fair AI algorithms, connecting and advancing the state of the art in statistical machine learning, fair AI, and human-centered 人工智能。该项目的第一条曲目将开发出“免费获得公平性”的方法，在这种方法中，在牺牲几乎没有性能的情况下发现了最公平的解决方案。研究人员将针对此任务设计黑框，灰色框和白色框方法。然后，研究的第二条轨道将重点放在开发可解释的AI和数据可视化技术上，以帮助人类评估和权衡不同公平竞争笔记的后果。实现此目的的关键步骤是创建一个统一的公平框架，该框架系统地编码了可能的公平指标的空间。最后，在项目的第三轨道中，研究人员将为AI公平的几种现实应用程序开发实用解决方案，包括医疗资源的分配和基于AI的职业咨询。解决方案将涉及应用和基础AI研究，并将支持对前两条曲目中开发的方法的评估。该项目还包括有关外展活动的举措，扩大参与科学，技术，工程和数学（STEM）领域，培训和教育学生的倡议以及课程发展。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来通过评估来获得支持的。

项目成果

Neural Embedding Allocation: Distributed Representations of Topic Models

• DOI: 10.1162/coli_a_00457
• 发表时间: 2019-09
• 期刊: Computational Linguistics
• 影响因子: 9.3
• 作者: Kamrun Keya;Yannis Papanikolaou;James R. Foulds

Do Humans Prefer Debiased AI Algorithms? A Case Study in Career Recommendation

人类更喜欢有偏差的人工智能算法吗？

• DOI: 10.1145/3490099.3511108
• 发表时间: 2022
• 期刊: Annual Conference on Intelligent User Interfaces (IUI
• 作者: Wang, Clarice;Wang, Kathryn;Bian, Andrew;Islam, Rashidul;Keya, Kamrun Naher;Foulds, James;Pan, Shimei
• 通讯作者: Pan, Shimei

Can We Obtain Fairness For Free?

我们能免费获得公平吗？

• DOI: 10.1145/3461702.3462614
• 发表时间: 2021
• 期刊: and Society 2021
• 作者: Islam, Rashidul;Pan, Shimei;Foulds, James R.
• 通讯作者: Foulds, James R.

When Biased Humans Meet Debiased AI: A Case Study in College Major Recommendation

• DOI: 10.1145/3611313
• 发表时间: 2023-09-01
• 期刊: ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS
• 影响因子: 3.4
• 作者: Wang，Clarice;Wang，Kathryn;Pan，Shimei
• 通讯作者: Pan，Shimei