Fair Conformal Classification via Learning Representation-Based Groups

• URL: http://arxiv.org/abs/2605.12195v1
• Date: Tue, 12 May 2026 14:37:17 GMT
• Title: Fair Conformal Classification via Learning Representation-Based Groups
• Abstract summary: This paper introduces a fair conformal inference framework for classification tasks.<n>By balancing effectiveness and efficiency in producing compact, informative prediction sets, our approach paves a practical pathway toward trustworthy machine learning.
• Score: 18.926790668827937
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conformal prediction methods provide statistically rigorous marginal coverage guarantees for machine learning models, but such guarantees fail to account for algorithmic biases, thereby undermining fairness and trust. This paper introduces a fair conformal inference framework for classification tasks. The proposed method constructs prediction sets that guarantee conditional coverage on adaptively identified subgroups, which can be implicitly defined through nonlinear feature combinations. By balancing effectiveness and efficiency in producing compact, informative prediction sets and ensuring adaptive equalized coverage across unfairly treated subgroups, our approach paves a practical pathway toward trustworthy machine learning. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of the framework.

Related papers

• A Generic Framework for Conformal Fairness [19.694445748424346]
  We formalize textitConformal Fairness, a notion of fairness using conformal predictors.<n>We provide a theoretically well-founded algorithm and associated framework to control for the gaps in coverage between different sensitive groups.
  arXiv  Detail & Related papers  (2025-05-22T01:41:12Z)
• Rectifying Conformity Scores for Better Conditional Coverage [75.73184036344908]
  We present a new method for generating confidence sets within the split conformal prediction framework.<n>Our method performs a trainable transformation of any given conformity score to improve conditional coverage while ensuring exact marginal coverage.
  arXiv  Detail & Related papers  (2025-02-22T19:54:14Z)
• Noise-Adaptive Conformal Classification with Marginal Coverage [53.74125453366155]
  We introduce an adaptive conformal inference method capable of efficiently handling deviations from exchangeability caused by random label noise.<n>We validate our method through extensive numerical experiments demonstrating its effectiveness on synthetic and real data sets.
  arXiv  Detail & Related papers  (2025-01-29T23:55:23Z)
• Weighted Aggregation of Conformity Scores for Classification [9.559062601251464]
  Conformal prediction is a powerful framework for constructing prediction sets with valid coverage guarantees.<n>We propose a novel approach that combines multiple score functions to improve the performance of conformal predictors.
  arXiv  Detail & Related papers  (2024-07-14T14:58:03Z)
• Probabilistic Conformal Prediction with Approximate Conditional Validity [81.30551968980143]
  We develop a new method for generating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution. Our method consistently outperforms existing approaches in terms of conditional coverage.
  arXiv  Detail & Related papers  (2024-07-01T20:44:48Z)
• Conformal Classification with Equalized Coverage for Adaptively Selected Groups [9.016173836219524]
  This paper introduces a conformal inference method to evaluate uncertainty in classification by generating prediction sets with valid coverage conditional on adaptively chosen features. We demonstrate the validity and effectiveness of this method on simulated and real data sets.
  arXiv  Detail & Related papers  (2024-05-23T23:32:37Z)
• Efficient Conformal Prediction under Data Heterogeneity [79.35418041861327]
  Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification. Existing approaches for tackling non-exchangeability lead to methods that are not computable beyond the simplest examples. This work introduces a new efficient approach to CP that produces provably valid confidence sets for fairly general non-exchangeable data distributions.
  arXiv  Detail & Related papers  (2023-12-25T20:02:51Z)
• Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical [66.57396042747706]
  Complementary-label learning is a weakly supervised learning problem. We propose a consistent approach that does not rely on the uniform distribution assumption. We find that complementary-label learning can be expressed as a set of negative-unlabeled binary classification problems.
  arXiv  Detail & Related papers  (2023-11-27T02:59:17Z)
• Contrastive Learning for Fair Representations [50.95604482330149]
  Trained classification models can unintentionally lead to biased representations and predictions. Existing debiasing methods for classification models, such as adversarial training, are often expensive to train and difficult to optimise. We propose a method for mitigating bias by incorporating contrastive learning, in which instances sharing the same class label are encouraged to have similar representations.
  arXiv  Detail & Related papers  (2021-09-22T10:47:51Z)
• Causal Feature Selection for Algorithmic Fairness [61.767399505764736]
  We consider fairness in the integration component of data management. We propose an approach to identify a sub-collection of features that ensure the fairness of the dataset.
  arXiv  Detail & Related papers  (2020-06-10T20:20:10Z)
• Classification with Valid and Adaptive Coverage [11.680355561258427]
  Conformal inference, cross-validation+, and the jackknife+ are hold-out methods that can be combined with virtually any machine learning algorithm. We develop specialized versions of these techniques for categorical and unordered response labels.
  arXiv  Detail & Related papers  (2020-06-03T21:42:04Z)

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.