Award Abstract # 2232055
Collaborative Research: RI: Small: End-to-end Learning of Fair and Explainable Schedules for Court Systems

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: WASHINGTON UNIVERSITY, THE
Initial Amendment Date: March 15, 2023
Latest Amendment Date: April 9, 2024
Award Number: 2232055
Award Instrument: Standard Grant
Program Manager: Andy Duan
yduan@nsf.gov
 (703)292-4286
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: August 1, 2023
End Date: July 31, 2026 (Estimated)
Total Intended Award Amount: $250,000.00
Total Awarded Amount to Date: $270,000.00
Funds Obligated to Date: FY 2023 = $250,000.00
FY 2024 = $20,000.00
History of Investigator:
  • William Yeoh (Principal Investigator)
    wyeoh@wustl.edu
Recipient Sponsored Research Office: Washington University
1 BROOKINGS DR
SAINT LOUIS
MO  US  63130-4862
(314)747-4134
Sponsor Congressional District: 01
Primary Place of Performance: Washington University
ONE BROOKINGS DR
SAINT LOUIS
MO  US  63130-4862
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): L6NFUM28LQM5
Parent UEI:
NSF Program(s): Robust Intelligence
Primary Program Source: 01002324DB NSF RESEARCH & RELATED ACTIVIT
01002425DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7495, 7923, 9251
Program Element Code(s): 749500
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Vasileiou, Stylianos and Xu, Borung and Yeoh, William "A Logic-based Framework for Explainable Agent Scheduling Problems" Proceedings of the European Conference on Artificial Intelligence (ECAI) , 2023 https://doi.org/10.3233/FAIA230542 Citation Details
Vasileiou, Stylianos and Yeoh, William "PLEASE: Generating Personalized Explanations in Human-Aware Planning" Proceedings of the European Conference on Artificial Intelligence (ECAI) , 2023 https://doi.org/10.3233/FAIA230543 Citation Details

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