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Policy Optimization for Long-Term Fairness in Decision Systems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Policy Optimization for Long-Term Fairness in Decision Systems./
Author:	Yu, Eric Yang.
Description:	1 online resource (36 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798383087060

Policy Optimization for Long-Term Fairness in Decision Systems.
Yu, Eric Yang.

Policy Optimization for Long-Term Fairness in Decision Systems. - 1 online resource (36 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--University of California, San Diego, 2024.

Includes bibliographical references

Long-term fairness is an important factor of consideration in designing and deploying learning-based decision systems in high-stake decision-making contexts. Recent work has proposed the use of Markov Decision Processes (MDPs) to formulate decision-making with long-term fairness requirements in dynamically changing environments, and demonstrated major challenges in directly deploying heuristic and rule-based policies that worked well in static environments. We show that policy optimization methods from deep reinforcement learning can be used to find strictly better decision policies that can often achieve both higher overall utility and less violation of the fairness requirements, compared to previously-known strategies. In particular, we propose new methods for imposing fairness requirements in policy optimization by regularizing the advantage evaluation of different actions. Our proposed methods make it easy to impose fairness constraints without reward engineering or sacrificing training efficiency. We perform detailed analyses in three established case studies, including attention allocation in incident monitoring, bank loan approval, and vaccine distribution in population networks.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383087060Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Markov Decision ProcessesIndex Terms--Genre/Form:

554714
Electronic books.

Policy Optimization for Long-Term Fairness in Decision Systems.
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520 $a Long-term fairness is an important factor of consideration in designing and deploying learning-based decision systems in high-stake decision-making contexts. Recent work has proposed the use of Markov Decision Processes (MDPs) to formulate decision-making with long-term fairness requirements in dynamically changing environments, and demonstrated major challenges in directly deploying heuristic and rule-based policies that worked well in static environments. We show that policy optimization methods from deep reinforcement learning can be used to find strictly better decision policies that can often achieve both higher overall utility and less violation of the fairness requirements, compared to previously-known strategies. In particular, we propose new methods for imposing fairness requirements in policy optimization by regularizing the advantage evaluation of different actions. Our proposed methods make it easy to impose fairness constraints without reward engineering or sacrificing training efficiency. We perform detailed analyses in three established case studies, including attention allocation in incident monitoring, bank loan approval, and vaccine distribution in population networks.
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