Collaborative Research: RI: Small: End-to-end Learning of Fair and Explainable Schedules for Court Systems

合作研究：RI：小型：法院系统公平且可解释的时间表的端到端学习

• 批准号: 2232054
• 负责人: Ferdinando Fioretto
• 金额: $ 35万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2232054&HistoricalAwards=false
• 关键词: Collaborative; Research; RI; Small; End

The American Court system is a large and complex socio-technical system that handles millions of criminal cases every year. However, the current pretrial scheduling process is plagued by a staggering one in five defendants missing court dates. This imposes high costs on the judiciary as an institution, and can be particularly harmful to defendants who have insecure employment situations, care-giving responsibilities, or lack transportation to court. These disparate impacts have profound negative effects. To address these issues, this project investigates Fair and Explainable Learning to Schedule, a novel approach that tightly integrates machine learning, constrained optimization, and knowledge representation to learn schedules with certifiable fairness guarantees and enable neuro-symbolic reasoning to provide meaningful and refinable explanations. The proposed research will develop new tools to ensure that pretrial scheduling can decrease nonappearance and be fair to all defendants equally and has thus the potential to have significant societal benefits.From a scientific standpoint, this project will develop a new generation of integrated learning and optimization tools as well as explanation tools to realize the potential of fairer and more equitable schedules. The proposed Fair and Explainable Learning to Schedule will make key contributions in several areas, including: (1) enabling deep learning systems to handle combinatorial structures to represent schedules; (2) developing end-to-end training procedures that integrate constrained optimization within a learning pipeline; (3) providing guarantees on the satisfaction of user-specified fairness notions in the learning process; (4) developing neuro-symbolic approaches to provide explanations about scheduling and fairness properties; (5) integrating learning and logic-based reasoning to provide personalized explanations at appropriate abstraction levels to users; and (6) developing new datasets for fair pretrial court scheduling.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.

美国法院系统是一个庞大而复杂的社会技术系统，每年处理数百万起刑事案件。然而，目前的审前日程安排过程受到令人震惊的五分之一被告缺席开庭日期的困扰。这给司法机构带来了高昂的费用，对那些就业不稳定、负有照顾责任或缺乏前往法院的交通工具的被告尤其有害。这些截然不同的影响产生了深远的负面影响。为了解决这些问题，该项目研究了Fair and Explainable Learning to Schedule，这是一种将机器学习，约束优化和知识表示紧密集成的新方法，可以学习具有可证明公平性保证的时间表，并使神经符号推理能够提供有意义和可改进的解释。该研究将开发新的工具，以确保审前时间安排可以减少不出庭，并公平对待所有被告，从而有可能产生重大的社会效益。从科学的角度来看，该项目将开发新一代的综合学习和优化工具以及解释工具，以实现更公平和更公正的时间表的潜力。Fair and Explainable Learning to Schedule将在几个领域做出重要贡献，包括：（1）使深度学习系统能够处理组合结构来表示时间表;（2）开发端到端的训练程序，将约束优化集成到学习管道中;（3）在学习过程中提供对用户指定的公平性概念的满足保证;（4）在学习过程中提供对用户指定的公平性概念的满足保证。（4）开发神经符号方法以提供关于调度和公平性的解释：（5）集成学习和基于逻辑的推理以在适当的抽象级别上向用户提供个性化的解释;以及（6）该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。

项目成果

End-to-End Optimization and Learning for Multiagent Ensembles

• DOI: 10.48550/arxiv.2211.00251
• 发表时间: 2023
• 期刊: ArXiv
• 作者: James Kotary;Vincenzo Di Vito;Ferdinando Fioretto

Differentiable Model Selection for Ensemble Learning

• DOI: 10.24963/ijcai.2023/217
• 发表时间: 2022-11
• 作者: James Kotary;Vincenzo Di Vito;Ferdinando Fioretto

Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization

分析和增强展开优化的后向收敛性

• 发表时间: 2023
• 期刊: arXivorg
• 作者: Kotary, James;Christopher, Jacob;Dinh, My H;Fioretto, Ferdinando
• 通讯作者: Fioretto, Ferdinando