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：
2232055
负责人：
William Yeoh
金额：
$ 25万
依托单位：
Washington 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 2026-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2232055&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.

美国法院制度是一个大型且复杂的社会技术系统，每年处理数百万个刑事案件。但是，目前的审前调度程序受到五分之一的被告缺少法院日期的惊人。这将司法机构作为一家机构施加了高昂的成本，并且对那些不安全的就业情况，提供护理责任或缺乏向法院运输的被告尤其有害。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，该项目调查了安排公平和可解释的学习，这是一种紧密整合机器学习，约束优化和知识表示的新颖方法，以学习时间表，并具有可证明的公平性保证，并启用神经符号的推理，以提供有意义的可用解释。拟议的研究将开发新的工具，以确保审前调度可以降低非表现，并对所有被告平等地公平，因此具有具有重大社会利益的潜力。从科学的角度来看，该项目将开发出新一代的综合学习和优化工具，以及解释工具的解释工具，以实现更加公平和更公平的时间表。拟议的公平和可解释的学习安排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组合结构以表示时间表；（2）制定端到端培训程序，以整合学习管道中的约束优化；（3）在学习过程中对用户指定的公平概念满意的保证；（4）开发神经符号方法来提供有关调度和公平性能的解释；（5）整合学习和基于逻辑的推理，以向用户提供适当的抽象级别的个性化解释；（6）为公平审前法院安排制定新的数据集。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来评估值得支持的。