A Causal Framework to Measure and Mitigate Non-binary Treatment Discrimination

• URL: http://arxiv.org/abs/2503.22454v1
• Date: Fri, 28 Mar 2025 14:06:35 GMT
• Title: A Causal Framework to Measure and Mitigate Non-binary Treatment Discrimination
• Authors: Ayan Majumdar, Deborah D. Kanubala, Kavya Gupta, Isabel Valera,
• Abstract summary: We argue that non-binary treatment decisions are integral to the decision process and controlled by decision-makers.<n>We propose a causal framework that extends fairness analyses.<n>We show that our framework effectively mitigates treatment discrimination from historical data.
• Score: 10.30435055404255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fairness studies of algorithmic decision-making systems often simplify complex decision processes, such as bail or loan approvals, into binary classification tasks. However, these approaches overlook that such decisions are not inherently binary (e.g., approve or not approve bail or loan); they also involve non-binary treatment decisions (e.g., bail conditions or loan terms) that can influence the downstream outcomes (e.g., loan repayment or reoffending). In this paper, we argue that non-binary treatment decisions are integral to the decision process and controlled by decision-makers and, therefore, should be central to fairness analyses in algorithmic decision-making. We propose a causal framework that extends fairness analyses and explicitly distinguishes between decision-subjects' covariates and the treatment decisions. This specification allows decision-makers to use our framework to (i) measure treatment disparity and its downstream effects in historical data and, using counterfactual reasoning, (ii) mitigate the impact of past unfair treatment decisions when automating decision-making. We use our framework to empirically analyze four widely used loan approval datasets to reveal potential disparity in non-binary treatment decisions and their discriminatory impact on outcomes, highlighting the need to incorporate treatment decisions in fairness assessments. Moreover, by intervening in treatment decisions, we show that our framework effectively mitigates treatment discrimination from historical data to ensure fair risk score estimation and (non-binary) decision-making processes that benefit all stakeholders.

Related papers

• (Un)certainty of (Un)fairness: Preference-Based Selection of Certainly Fair Decision-Makers [0.0]
  Fairness metrics are used to assess discrimination and bias in decision-making processes across various domains. We quantify the uncertainty of the disparity to enhance discrimination assessments. We define preferences over decision-makers and utilize brute-force to choose the optimal decision-maker.
  arXiv  Detail & Related papers  (2024-09-19T11:44:03Z)
• Enhancing Language Model Rationality with Bi-Directional Deliberation Reasoning [73.77288647011295]
  This paper introduces BI-Directional DEliberation Reasoning (BIDDER) to enhance the decision rationality of language models. Our approach involves three key processes:. Inferring hidden states to represent uncertain information in the decision-making process from historical data;. Using hidden states to predict future potential states and potential outcomes;. Integrating historical information (past contexts) and long-term outcomes (future contexts) to inform reasoning.
  arXiv  Detail & Related papers  (2024-07-08T16:48:48Z)
• Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning [72.80902932543474]
  Understanding human behavior from observed data is critical for transparency and accountability in decision-making. Consider real-world settings such as healthcare, in which modeling a decision-maker's policy is challenging. We propose a data-driven representation of decision-making behavior that inheres transparency by design, accommodates partial observability, and operates completely offline.
  arXiv  Detail & Related papers  (2023-10-28T13:06:14Z)
• Online Decision Mediation [72.80902932543474]
  Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior. In clinical diagnosis, fully-autonomous machine behavior is often beyond ethical affordances.
  arXiv  Detail & Related papers  (2023-10-28T05:59:43Z)
• Influence of the algorithm's reliability and transparency in the user's decision-making process [0.0]
  We conduct an online empirical study with 61 participants to find out how the change in transparency and reliability of an algorithm could impact users' decision-making process. The results indicate that people show at least moderate confidence in the decisions of the algorithm even when the reliability is bad.
  arXiv  Detail & Related papers  (2023-07-13T03:13:49Z)
• An End-to-End Approach for Online Decision Mining and Decision Drift Analysis in Process-Aware Information Systems: Extended Version [0.0]
  Decision mining enables the discovery of decision rules from event logs or streams. Online decision mining enables continuous monitoring of decision rule evolution and decision drift. This paper presents an end-to-end approach for the discovery as well as monitoring of decision points and the corresponding decision rules during runtime.
  arXiv  Detail & Related papers  (2023-03-07T15:04:49Z)
• 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)
• A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs [0.0]
  In prediction-based decision-making systems, different perspectives can be at odds. The short-term business goals of the decision makers are often in conflict with the decision subjects' wish to be treated fairly. We propose a framework to make these value-laden choices clearly visible.
  arXiv  Detail & Related papers  (2022-06-06T20:31:55Z)
• Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
  We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
  arXiv  Detail & Related papers  (2022-03-14T17:40:42Z)
• Learning Pareto-Efficient Decisions with Confidence [21.915057426589748]
  The paper considers the problem of multi-objective decision support when outcomes are uncertain. This enables quantifying trade-offs between decisions in terms of tail outcomes that are relevant in safety-critical applications.
  arXiv  Detail & Related papers  (2021-10-19T11:32:17Z)
• Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
  We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap. We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert. Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
  arXiv  Detail & Related papers  (2021-01-24T05:40:29Z)

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.