Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making

• URL: http://arxiv.org/abs/2312.04772v4
• Date: Wed, 19 Jun 2024 20:29:19 GMT
• Title: Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making
• Authors: Parand A. Alamdari, Toryn Q. Klassen, Elliot Creager, Sheila A. McIlraith,
• Abstract summary: We explore the notion of non-Markovian fairness in the context of sequential decision making. We identify properties of non-Markovian fairness, including notions of long-term, anytime, periodic, and bounded fairness. We introduce the FairQCM algorithm, which can automatically augment its training data to improve sample efficiency in the synthesis of fair policies.
• Score: 15.289872878202399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fair decision making has largely been studied with respect to a single decision. Here we investigate the notion of fairness in the context of sequential decision making where multiple stakeholders can be affected by the outcomes of decisions. We observe that fairness often depends on the history of the sequential decision-making process, and in this sense that it is inherently non-Markovian. We further observe that fairness often needs to be assessed at time points within the process, not just at the end of the process. To advance our understanding of this class of fairness problems, we explore the notion of non-Markovian fairness in the context of sequential decision making. We identify properties of non-Markovian fairness, including notions of long-term, anytime, periodic, and bounded fairness. We explore the interplay between non-Markovian fairness and memory and how memory can support construction of fair policies. Finally, we introduce the FairQCM algorithm, which can automatically augment its training data to improve sample efficiency in the synthesis of fair policies via reinforcement learning.

Related papers

• Temporal Fairness in Decision Making Problems [13.110642125062428]
  We focus on how to reason over fairness from a temporal perspective, taking into account the fairness of a history of past decisions. We propose three approaches that incorporate temporal fairness in decision making problems formulated as optimization problems.
  arXiv  Detail & Related papers  (2024-08-23T16:36:58Z)
• Fairness-Accuracy Trade-Offs: A Causal Perspective [58.06306331390586]
  We analyze the tension between fairness and accuracy from a causal lens for the first time. We show that enforcing a causal constraint often reduces the disparity between demographic groups. We introduce a new neural approach for causally-constrained fair learning.
  arXiv  Detail & Related papers  (2024-05-24T11:19:52Z)
• The Unfairness of $\varepsilon$-Fairness [0.0]
  We show that if the concept of $varepsilon$-fairness is employed, it can possibly lead to outcomes that are maximally unfair in the real-world context. We illustrate our findings with two real-world examples: college admissions and credit risk assessment.
  arXiv  Detail & Related papers  (2024-05-15T14:13:35Z)
• What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning [52.51430732904994]
  In reinforcement learning problems, agents must consider long-term fairness while maximizing returns. Recent works have proposed many different types of fairness notions, but how unfairness arises in RL problems remains unclear. We introduce a novel notion called dynamics fairness, which explicitly captures the inequality stemming from environmental dynamics.
  arXiv  Detail & Related papers  (2024-04-16T22:47:59Z)
• Causal Fairness for Outcome Control [68.12191782657437]
  We study a specific decision-making task called outcome control in which an automated system aims to optimize an outcome variable $Y$ while being fair and equitable. In this paper, we first analyze through causal lenses the notion of benefit, which captures how much a specific individual would benefit from a positive decision. We then note that the benefit itself may be influenced by the protected attribute, and propose causal tools which can be used to analyze this.
  arXiv  Detail & Related papers  (2023-06-08T09:31:18Z)
• Fairness in Matching under Uncertainty [78.39459690570531]
  algorithmic two-sided marketplaces have drawn attention to the issue of fairness in such settings. We axiomatize a notion of individual fairness in the two-sided marketplace setting which respects the uncertainty in the merits. We design a linear programming framework to find fair utility-maximizing distributions over allocations.
  arXiv  Detail & Related papers  (2023-02-08T00:30:32Z)
• How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts [107.72786199113183]
  We propose a novel fairness learning method termed CUrvature MAtching (CUMA) CUMA achieves robust fairness generalizable to unseen domains with unknown distributional shifts. We evaluate our method on three popular fairness datasets.
  arXiv  Detail & Related papers  (2022-07-04T02:37:50Z)
• On Learning and Testing of Counterfactual Fairness through Data Preprocessing [27.674565351048077]
  Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly. Recent work brings the discussion of machine learning fairness into the causal framework and elaborates on the concept of Counterfactual Fairness. We develop the Fair Learning through dAta Preprocessing (FLAP) algorithm to learn counterfactually fair decisions from biased training data.
  arXiv  Detail & Related papers  (2022-02-25T00:21:46Z)
• Algorithmic Decision Making with Conditional Fairness [48.76267073341723]
  We define conditional fairness as a more sound fairness metric by conditioning on the fairness variables. We propose a Derivable Conditional Fairness Regularizer (DCFR) to track the trade-off between precision and fairness of algorithmic decision making.
  arXiv  Detail & Related papers  (2020-06-18T12:56:28Z)
• Statistical Equity: A Fairness Classification Objective [6.174903055136084]
  We propose a new fairness definition motivated by the principle of equity. We formalize our definition of fairness, and motivate it with its appropriate contexts. We perform multiple automatic and human evaluations to show the effectiveness of our definition.
  arXiv  Detail & Related papers  (2020-05-14T23:19:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.