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.

这项研究项目的目标是开发机器学习技术，以公平分配医疗服务，如医疗补助提供的服务。尽管这些方案为弱势群体提供了至关重要的服务，但由于资源有限，许多最需要这些服务的人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，然后可以用来确定等待名单的优先顺序。向有需要的人提供护理可以防止这些人被制度化，这既提高了生活质量，又降低了总成本。虽然这种做法的好处是显而易见的，但必须注意确保确定优先次序的过程是公平的。研究人员还计划通过为学习排名的任务开发公平定义和相应的公平学习算法来直接解决这个问题。拟议的公平优先医疗保健技术有可能拯救生命，也可以节省纳税人的钱。该项目旨在为马里兰州的医疗补助优先顺序带来一个部署的解决方案，根据政府责任基金会2018年的一份报告，自该州根据《平价医疗法案》开始扩大医疗补助计划以来，该州已有超过8000人在医疗补助等待名单上死亡。该项目将开发一种机器学习干预，按照接受医疗服务的优先顺序对个人进行排名。研究人员将把他们的方法应用于医疗补助数据，他们将通过与Hilltop Institute的同事持续合作来获取这些数据；Hilltop Institute是一个无党派的研究组织，致力于面向社区的医疗分析；他们还将在公共数据集上评估他们的方法，以促进研究的重复性。该项目的一个关键目标是促进排名的公平。为了实现这一目标，该项目将把公平的机器学习定义和算法的能力扩展到以前没有解决的任务，包括生存和时间建模。为了预测个人的健康状况，研究团队将使用生存模型来估计未来住院的风险，例如重新安置到养老院。该团队还将使用考克斯比例风险模型；协变量和风险之间的乘法关系将有助于解释。这些模型的公平定义和相应的公平学习算法将产生风险分数，然后可以使用该分数来确定等待名单的优先顺序。对于实际部署的等候名单，由于个人进入和离开名单(例如，由于死亡或住院)，以及由于留在名单上的协变量发生变化，因此有必要不断地对名单进行重新排序；重新排序应确保需要护理的个人最终将排在名单的前面。这项拟议的工作跨越了多个学科(机器学习、公平、医疗IT、女权主义和民权)的边界，以解决一个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智力优势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

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