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

基本信息

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

项目摘要

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，目前的审前安排过程受到五分之一被告缺席开庭日期的困扰。这给司法机构作为一个机构带来了高昂的成本，并且对于就业状况不稳定、有照顾责任或缺乏前往法庭的交通的被告尤其有害。这些不同的影响会产生深远的负面影响。为了解决这些问题，该项目研究了“公平且可解释的学习计划”，这是一种紧密集成机器学习、约束优化和知识表示的新颖方法，以通过可证明的公平性保证来学习计划，并使神经符号推理能够提供有意义且可完善的解释。拟议的研究将开发新的工具，以确保审前安排能够减少缺席情况，并平等地对待所有被告，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集成学习和优化工具以及解释工具，以实现更公平和更公平的时间表的潜力。拟议的“公平且可解释的学习计划”将在多个领域做出关键贡献，包括：（1）使深度学习系统能够处理代表计划的组合结构； (2) 开发将约束优化集成到学习管道中的端到端训练程序；（3）为学习过程中用户指定的公平观念的满足提供保证； (4) 开发神经符号方法来提供有关调度和公平性属性的解释；（5）整合学习和基于逻辑的推理，以适当的抽象级别向用户提供个性化解释； (6) 开发新的数据集以实现公平的审前法庭安排。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。