Fairness Perceptions in Regression-based Predictive Models

• URL: http://arxiv.org/abs/2505.04886v2
• Date: Fri, 09 May 2025 20:00:10 GMT
• Title: Fairness Perceptions in Regression-based Predictive Models
• Authors: Mukund Telukunta, Venkata Sriram Siddhardh Nadendla, Morgan Stuart, Casey Canfield,
• Abstract summary: This paper introduces three novel divergence-based group fairness notions to assess the fairness of regression-based analytics tools.<n>Crowd feedback is used to estimate social fairness preferences.<n>The findings clearly depict a strong preference towards the separation and sufficiency fairness notions, and that the predictive analytics is deemed fair with respect to gender and race groups, but unfair in terms of age groups.
• Score: 0.19999259391104385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Regression-based predictive analytics used in modern kidney transplantation is known to inherit biases from training data. This leads to social discrimination and inefficient organ utilization, particularly in the context of a few social groups. Despite this concern, there is limited research on fairness in regression and its impact on organ utilization and placement. This paper introduces three novel divergence-based group fairness notions: (i) independence, (ii) separation, and (iii) sufficiency to assess the fairness of regression-based analytics tools. In addition, fairness preferences are investigated from crowd feedback, in order to identify a socially accepted group fairness criterion for evaluating these tools. A total of 85 participants were recruited from the Prolific crowdsourcing platform, and a Mixed-Logit discrete choice model was used to model fairness feedback and estimate social fairness preferences. The findings clearly depict a strong preference towards the separation and sufficiency fairness notions, and that the predictive analytics is deemed fair with respect to gender and race groups, but unfair in terms of age groups.

Related papers

• Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone [3.3298048942057523]
  We propose a fairness-based grouping approach for continuous (possibly multidimensional) sensitive attributes.<n>By grouping data according to observed levels of discrimination, our method identifies the partition that maximizes a novel criterion.<n>We validate the proposed approach using multiple synthetic datasets and demonstrate its robustness under changing population distributions.
  arXiv  Detail & Related papers  (2025-07-15T12:21:52Z)
• Identifying and Mitigating Social Bias Knowledge in Language Models [52.52955281662332]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases.<n>FAST surpasses state-of-the-art baselines with superior debiasing performance.<n>This highlights the potential of fine-grained debiasing strategies to achieve fairness in large language models.
  arXiv  Detail & Related papers  (2024-08-07T17:14:58Z)
• Parametric Fairness with Statistical Guarantees [0.46040036610482665]
  We extend the concept of Demographic Parity to incorporate distributional properties in predictions, allowing expert knowledge to be used in the fair solution. We illustrate the use of this new metric through a practical example of wages, and develop a parametric method that efficiently addresses practical challenges.
  arXiv  Detail & Related papers  (2023-10-31T14:52:39Z)
• Learning for Counterfactual Fairness from Observational Data [62.43249746968616]
  Fairness-aware machine learning aims to eliminate biases of learning models against certain subgroups described by certain protected (sensitive) attributes such as race, gender, and age. A prerequisite for existing methods to achieve counterfactual fairness is the prior human knowledge of the causal model for the data. In this work, we address the problem of counterfactually fair prediction from observational data without given causal models by proposing a novel framework CLAIRE.
  arXiv  Detail & Related papers  (2023-07-17T04:08:29Z)
• Reconciling Predictive and Statistical Parity: A Causal Approach [68.59381759875734]
  We propose a new causal decomposition formula for the fairness measures associated with predictive parity. We show that the notions of statistical and predictive parity are not really mutually exclusive, but complementary and spanning a spectrum of fairness notions.
  arXiv  Detail & Related papers  (2023-06-08T09:23:22Z)
• The Flawed Foundations of Fair Machine Learning [0.0]
  We show that there is a trade-off between statistically accurate outcomes and group similar outcomes in any data setting where group disparities exist. We introduce a proof-of-concept evaluation to aid researchers and designers in understanding the relationship between statistically accurate outcomes and group similar outcomes.
  arXiv  Detail & Related papers  (2023-06-02T10:07:12Z)
• Counterpart Fairness -- Addressing Systematic between-group Differences in Fairness Evaluation [17.495053606192375]
  When using machine learning to aid decision-making, it is critical to ensure that an algorithmic decision is fair and does not discriminate against specific individuals/groups. Existing group fairness methods aim to ensure equal outcomes across groups delineated by protected variables like race or gender. In cases where systematic differences between groups play a significant role in outcomes, these methods may overlook the influence of non-protected variables.
  arXiv  Detail & Related papers  (2023-05-29T15:41:12Z)
• Error Parity Fairness: Testing for Group Fairness in Regression Tasks [5.076419064097733]
  This work presents error parity as a regression fairness notion and introduces a testing methodology to assess group fairness. It is followed by a suitable permutation test to compare groups on several statistics to explore disparities and identify impacted groups. Overall, the proposed regression fairness testing methodology fills a gap in the fair machine learning literature and may serve as a part of larger accountability assessments and algorithm audits.
  arXiv  Detail & Related papers  (2022-08-16T17:47:20Z)
• Measuring Fairness of Text Classifiers via Prediction Sensitivity [63.56554964580627]
  ACCUMULATED PREDICTION SENSITIVITY measures fairness in machine learning models based on the model's prediction sensitivity to perturbations in input features. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness.
  arXiv  Detail & Related papers  (2022-03-16T15:00:33Z)
• Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model Multiplicity [10.144058870887061]
  We argue that individuals can be harmed when one predictor is chosen ad hoc from a group of equally well performing models. Our findings suggest that such unfairness can be readily found in real life and it may be difficult to mitigate by technical means alone.
  arXiv  Detail & Related papers  (2022-03-14T14:33:39Z)
• Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach [131.20444904674494]
  We tackle the problem of measuring group fairness under unawareness of sensitive attributes. We show that quantification approaches are particularly suited to tackle the fairness-under-unawareness problem.
  arXiv  Detail & Related papers  (2021-09-17T13:45:46Z)
• Estimating and Improving Fairness with Adversarial Learning [65.99330614802388]
  We propose an adversarial multi-task training strategy to simultaneously mitigate and detect bias in the deep learning-based medical image analysis system. Specifically, we propose to add a discrimination module against bias and a critical module that predicts unfairness within the base classification model. We evaluate our framework on a large-scale public-available skin lesion dataset.
  arXiv  Detail & Related papers  (2021-03-07T03:10:32Z)

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.