AI-DCL: Fairness for the Allocation of Healthcare Resources

AI-DCL：医疗资源分配的公平性

• 批准号: 1927486
• 负责人: James Foulds
• 金额: $ 29.79万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1927486&HistoricalAwards=false
• 关键词: AI; DCL; Fairness; Allocation; Healthcare

The goal of this research project is to develop machine learning techniques for the fair allocation of healthcare services such as those provided by Medicaid. Although such programs provide crucial services to vulnerable populations, many of the individuals who most need these services languish on waiting lists due to limited resources. Machine learning models can potentially improve this situation by predicting individuals' levels of need, which can then be used to prioritize the waiting lists. Providing care to those in need can prevent institutionalization for those individuals, which both improves quality of life and reduces overall costs. While the benefits of such an approach are clear, care must be taken to ensure that the prioritization process is fair. The researchers also plan to address this issue directly by developing fairness definitions and corresponding fair learning algorithms for the task of learning to rank. The proposed techniques for fair prioritization of healthcare have the potential to save lives, as well as taxpayer dollars. This project aims to lead to a deployed solution for Medicaid prioritization in the state of Maryland, where over 8,000 individuals have died on the Medicaid waitlist since the state's Medicaid expansion under the Affordable Care Act began, according to a 2018 report from the Foundation for Government Accountability.This project will develop a machine learning intervention to the processes of ranking individuals in order of priority for receiving healthcare services. The researchers will apply their methods to Medicaid data, which they will access via their ongoing collaboration with colleagues from the Hilltop Institute, a nonpartisan research organization which is dedicated to community-oriented healthcare analytics; they will also evaluate their methods on a public dataset to facilitate research reproducibility. A key goal of the project is to promote fairness in the ranking. In meeting this goal, the project will extend the capabilities of fair machine learning definitions and algorithms to tasks that have not previously been addressed including survival and temporal modeling. To predict individuals' health status, the research team will use survival models to estimate the risk of future institutionalization, such as relocating to a nursing home. The team will use also Cox proportional hazard models; the multiplicative relationship between covariates and risk will serve to aid explainability. The fairness definitions and the corresponding fair learning algorithms for these models will yield risk scores that can then be used to prioritize waiting lists. For waitlists deployed in practice, it will be necessary to continually re-rank the list since individuals enter and leave the list (due to death or institutionalization, for example), and since covariates change for those who remain on the list; reranking should ensure that individuals who need care will eventually reach the front of the list. The proposed work crosses the boundaries of multiple disciplines (machine learning, fairness, health IT, feminism and civil rights) to solve an urgent real-world problem.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.

该研究项目的目标是开发机器学习技术，以公平分配医疗保健服务，例如医疗补助提供的服务。尽管此类计划为弱势群体提供了重要的服务，但由于资源有限，许多最需要这些服务的人都在等待名单上苦苦挣扎。机器学习模型可以通过预测个人的需求水平来潜在地改善这种情况，然后可以用它来确定等待名单的优先顺序。为有需要的人提供护理可以防止这些人被送入收容机构，从而提高生活质量并降低总体成本。虽然这种方法的好处是显而易见的，但必须注意确保优先级确定过程的公平性。研究人员还计划通过为学习排名任务开发公平性定义和相应的公平学习算法来直接解决这个问题。所提出的公平优先医疗保健技术有可能挽救生命以及纳税人的钱。该项目旨在为马里兰州的医疗补助优先提供部署解决方案，根据政府基金会 2018 年的一份报告，自马里兰州根据《平价医疗法案》开始扩大医疗补助范围以来，已有 8,000 多人在医疗补助候补名单上死亡。问责制。该项目将开发一种机器学习干预措施，按照接受医疗保健服务的优先顺序对个人进行排名。研究人员将他们的方法应用于医疗补助数据，他们将通过与 Hilltop Institute 的同事持续合作来访问这些数据，Hilltop Institute 是一个无党派研究组织，致力于面向社区的医疗保健分析；他们还将在公共数据集上评估他们的方法，以促进研究的可重复性。该项目的一个关键目标是促进排名的公平性。为了实现这一目标，该项目将把公平机器学习定义和算法的功能扩展到以前未解决的任务，包括生存和时间建模。为了预测个人的健康状况，研究小组将使用生存模型来估计未来住院治疗的风险，例如搬迁到疗养院。该团队还将使用 Cox 比例风险模型；协变量和风险之间的乘法关系将有助于解释。这些模型的公平性定义和相应的公平学习算法将产生风险评分，然后可用于对等待列表进行优先级排序。对于实践中部署的候补名单，由于个人进入和离开名单（例如，由于死亡或收容），并且由于留在名单上的人的协变量发生变化，因此有必要不断地对名单进行重新排名；重新排名应确保需要护理的个人最终会排在名单的前面。拟议的工作跨越了多个学科的界限（机器学习、公平、健康 IT、女权主义和公民权利），以解决紧迫的现实世界问题。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，认为值得支持。智力价值和更广泛的影响审查标准。

项目成果

Are Parity-Based Notions of {AI} Fairness Desirable?

基于奇偶校验的 {AI} 公平概念是否可取？

• 发表时间: 2020
• 期刊: A Quarterly bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering
• 作者: Foulds, J.R.;Pan, S.
• 通讯作者: Pan, S.

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

Equitable Allocation of Healthcare Resources with Fair Cox Models

利用公平考克斯模型公平分配医疗资源

• 发表时间: 2020
• 期刊: AAAI Fall Symposium on AI in Government and Public Sector (AAAI FSS-20
• 作者: Keya, K.;Islam, R.;Pan, S.;Stockwell, I.;Foulds, J. R.
• 通讯作者: Foulds, J. R.

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.

Equitable Allocation of Healthcare Resources with Fair Survival Models

以公平生存模式公平分配医疗资源

• DOI: 10.1137/1.9781611976700.22
• 发表时间: 2021
• 期刊: Proceedings of the 2021 SIAM International Conference on Data Mining (SDM 2021
• 作者: Keya, Kamrun Naher;Islam, Rashidul;Pan, Shimei;Stockwell, Ian;Foulds, James
• 通讯作者: Foulds, James