A Generic Framework for Conformal Fairness
- URL: http://arxiv.org/abs/2505.16115v1
- Date: Thu, 22 May 2025 01:41:12 GMT
- Title: A Generic Framework for Conformal Fairness
- Authors: Aditya T. Vadlamani, Anutam Srinivasan, Pranav Maneriker, Ali Payani, Srinivasan Parthasarathy,
- Abstract summary: 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.
- Score: 7.073917553857755
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Conformal Prediction (CP) is a popular method for uncertainty quantification with machine learning models. While conformal prediction provides probabilistic guarantees regarding the coverage of the true label, these guarantees are agnostic to the presence of sensitive attributes within the dataset. In this work, we formalize \textit{Conformal Fairness}, a notion of fairness using conformal predictors, and provide a theoretically well-founded algorithm and associated framework to control for the gaps in coverage between different sensitive groups. Our framework leverages the exchangeability assumption (implicit to CP) rather than the typical IID assumption, allowing us to apply the notion of Conformal Fairness to data types and tasks that are not IID, such as graph data. Experiments were conducted on graph and tabular datasets to demonstrate that the algorithm can control fairness-related gaps in addition to coverage aligned with theoretical expectations.
Related papers
- SConU: Selective Conformal Uncertainty in Large Language Models [59.25881667640868]
We propose a novel approach termed Selective Conformal Uncertainty (SConU)<n>We develop two conformal p-values that are instrumental in determining whether a given sample deviates from the uncertainty distribution of the calibration set at a specific manageable risk level.<n>Our approach not only facilitates rigorous management of miscoverage rates across both single-domain and interdisciplinary contexts, but also enhances the efficiency of predictions.
arXiv Detail & Related papers (2025-04-19T03:01:45Z) - 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) - Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores [52.92618442300405]
It is impossible to achieve exact, distribution-free conditional coverage in finite samples.<n>We propose an alternative conformal prediction algorithm that targets coverage where it matters most.
arXiv Detail & Related papers (2025-01-17T12:01:56Z) - Beyond Conformal Predictors: Adaptive Conformal Inference with Confidence Predictors [0.0]
Conformal prediction requires exchangeable data to ensure valid prediction sets at a user-specified significance level.
Adaptive conformal inference (ACI) was introduced to address this limitation.
We show that ACI does not require the use of conformal predictors; instead, it can be implemented with the more general confidence predictors.
arXiv Detail & Related papers (2024-09-23T21:02:33Z) - 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) - The Penalized Inverse Probability Measure for Conformal Classification [0.5172964916120902]
The work introduces the Penalized Inverse Probability (PIP) nonconformity score, and its regularized version RePIP, that allow the joint optimization of both efficiency and informativeness.
The work shows how PIP-based conformal classifiers exhibit precisely the desired behavior in comparison with other nonconformity measures and strike a good balance between informativeness and efficiency.
arXiv Detail & Related papers (2024-06-13T07:37:16Z) - Robust Conformal Prediction Using Privileged Information [17.886554223172517]
We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data.<n>Our approach builds on conformal prediction, a powerful framework to construct prediction sets that are valid under the i.i.d assumption.
arXiv Detail & Related papers (2024-06-08T08:56:47Z) - 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) - Federated Conformal Predictors for Distributed Uncertainty
Quantification [83.50609351513886]
Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning.
In this paper, we extend conformal prediction to the federated learning setting.
We propose a weaker notion of partial exchangeability, better suited to the FL setting, and use it to develop the Federated Conformal Prediction framework.
arXiv Detail & Related papers (2023-05-27T19:57:27Z) - Approximate Conditional Coverage via Neural Model Approximations [0.030458514384586396]
We analyze a data-driven procedure for obtaining empirically reliable approximate conditional coverage.
We demonstrate the potential for substantial (and otherwise unknowable) under-coverage with split-conformal alternatives with marginal coverage guarantees.
arXiv Detail & Related papers (2022-05-28T02:59:05Z) - Predictive Inference with Weak Supervision [3.1925030748447747]
We bridge the gap between partial supervision and validation by developing a conformal prediction framework.
We introduce a new notion of coverage and predictive validity, then develop several application scenarios.
We corroborate the hypothesis that the new coverage definition allows for tighter and more informative (but valid) confidence sets.
arXiv Detail & Related papers (2022-01-20T17:26:52Z)
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.