Fairness under uncertainty in sequential decisions

• URL: http://arxiv.org/abs/2604.21711v1
• Date: Thu, 23 Apr 2026 14:17:58 GMT
• Title: Fairness under uncertainty in sequential decisions
• Abstract summary: Algorithmic approaches alone cannot resolve structural inequalities.<n>Many real machine learning applications are online and sequential, with prior decisions informing future ones.<n>This paper introduces a taxonomy of uncertainty in sequential decision-making.
• Score: 14.392931077157906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fair machine learning (ML) methods help identify and mitigate the risk that algorithms encode or automate social injustices. Algorithmic approaches alone cannot resolve structural inequalities, but they can support socio-technical decision systems by surfacing discriminatory biases, clarifying trade-offs, and enabling governance. Although fairness is well studied in supervised learning, many real ML applications are online and sequential, with prior decisions informing future ones. Each decision is taken under uncertainty due to unobserved counterfactuals and finite samples, with dire consequences for under-represented groups, systematically under-observed due to historical exclusion and selective feedback. A bank cannot know whether a denied loan would have been repaid, and may have less data on marginalized populations. This paper introduces a taxonomy of uncertainty in sequential decision-making -- model, feedback, and prediction uncertainty -- providing shared vocabulary for assessing systems where uncertainty is unevenly distributed across groups. We formalize model and feedback uncertainty via counterfactual logic and reinforcement learning, and illustrate harms to decision makers (unrealized gains/losses) and subjects (compounding exclusion, reduced access) of policies that ignore the unobserved space. Algorithmic examples show it is possible to reduce outcome variance for disadvantaged groups while preserving institutional objectives (e.g. expected utility). Experiments on data simulated with varying bias show how unequal uncertainty and selective feedback produce disparities, and how uncertainty-aware exploration alters fairness metrics. The framework equips practitioners to diagnose, audit, and govern fairness risks. Where uncertainty drives unfairness rather than incidental noise, accounting for it is essential to fair and effective decision-making.

Related papers

• Partial Identification Approach to Counterfactual Fairness Assessment [50.88100567472179]
  We introduce a Bayesian approach to bound unknown counterfactual fairness measures with high confidence.<n>Our results reveal a positive (spurious) effect on the COMPAS score when changing race to African-American (from all others) and a negative (direct causal) effect when transitioning from young to old age.
  arXiv  Detail & Related papers  (2025-09-30T18:35:08Z)
• Interpretable and Fair Mechanisms for Abstaining Classifiers [12.539170258479778]
  We introduce Interpretable and Fair Abstaining, an algorithm that can reject both predictions based on uncertainty and their unfairness.<n>Our method reduces error and positive decision rate differences across demographic groups the non-rejected data.<n>This explainable aspect is especially important in light of recent AI regulations, mandating that any high-risk decision task should be overseen by human experts to reduce discrimination risks.
  arXiv  Detail & Related papers  (2025-03-24T16:06:43Z)
• Probabilistic Modeling of Disparity Uncertainty for Robust and Efficient Stereo Matching [61.73532883992135]
  We propose a new uncertainty-aware stereo matching framework.<n>We adopt Bayes risk as the measurement of uncertainty and use it to separately estimate data and model uncertainty.
  arXiv  Detail & Related papers  (2024-12-24T23:28:20Z)
• Auditing Fairness under Unobserved Confounding [56.61738581796362]
  We show that, surprisingly, one can still compute meaningful bounds on treatment rates for high-risk individuals.<n>We use the fact that in many real-world settings we have data from prior to any allocation to derive unbiased estimates of risk.
  arXiv  Detail & Related papers  (2024-03-18T21:09:06Z)
• Equal Confusion Fairness: Measuring Group-Based Disparities in Automated Decision Systems [5.076419064097733]
  This paper proposes a new equal confusion fairness test to check an automated decision system for fairness and a new confusion parity error to quantify the extent of any unfairness. Overall, the methods and metrics provided here may assess automated decision systems' fairness as part of a more extensive accountability assessment.
  arXiv  Detail & Related papers  (2023-07-02T04:44:19Z)
• Fair Decision-making Under Uncertainty [1.5688552250473473]
  We study a longitudinal censored learning problem subject to fairness constraints. We show how the newly devised fairness notions involving censored information and the general framework for fair predictions in the presence of censorship allow us to measure and discrimination under uncertainty.
  arXiv  Detail & Related papers  (2023-01-29T05:42:39Z)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions [4.159343412286401]
  We argue that algorithmic unfairness stems from interactions between models and biases in the data. We study a set of hypotheses regarding the fairness-accuracy trade-offs that fairness-blind ML algorithms exhibit under different data bias settings.
  arXiv  Detail & Related papers  (2022-07-13T15:18:30Z)
• Multi-Stage Decentralized Matching Markets: Uncertain Preferences and Strategic Behaviors [91.3755431537592]
  This article develops a framework for learning optimal strategies in real-world matching markets. We show that there exists a welfare-versus-fairness trade-off that is characterized by the uncertainty level of acceptance. We prove that participants can be better off with multi-stage matching compared to single-stage matching.
  arXiv  Detail & Related papers  (2021-02-13T19:25:52Z)
• Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty [66.17147341354577]
  We argue for considering a complementary form of transparency by estimating and communicating the uncertainty associated with model predictions. We describe how uncertainty can be used to mitigate model unfairness, augment decision-making, and build trustworthy systems. This work constitutes an interdisciplinary review drawn from literature spanning machine learning, visualization/HCI, design, decision-making, and fairness.
  arXiv  Detail & Related papers  (2020-11-15T17:26:14Z)

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.