Explainable post-training bias mitigation with distribution-based fairness metrics

• URL: http://arxiv.org/abs/2504.01223v3
• Date: Wed, 29 Oct 2025 18:37:50 GMT
• Title: Explainable post-training bias mitigation with distribution-based fairness metrics
• Authors: Ryan Franks, Alexey Miroshnikov, Konstandinos Kotsiopoulos,
• Abstract summary: We develop a novel bias mitigation framework with distribution-based fairness constraints for demographically blind and explainable machine-learning models.<n>This is accomplished through post-processing, allowing fairer models to be generated efficiently without retraining the underlying model.<n>We empirically test our methodology on a variety of datasets and compare it with alternative post-processing approaches.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop a novel bias mitigation framework with distribution-based fairness constraints suitable for producing demographically blind and explainable machine-learning models across a wide range of fairness levels. This is accomplished through post-processing, allowing fairer models to be generated efficiently without retraining the underlying model. Our framework, which is based on stochastic gradient descent, can be applied to a wide range of model types, with a particular emphasis on the post-processing of gradient-boosted decision trees. Additionally, we design a broad family of global fairness metrics, along with differentiable and consistent estimators compatible with our framework, building on previous work. We empirically test our methodology on a variety of datasets and compare it with alternative post-processing approaches, including Bayesian search, optimal transport projection, and direct neural network training.

Related papers

• On the use of graph models to achieve individual and group fairness [0.6299766708197883]
  We provide a theoretical framework based on Sheaf Diffusion to leverage tools based on dynamical systems and homology to model fairness.<n>We present a collection of network topologies handling different fairness metrics, leading to a unified method capable of dealing with both individual and group bias.<n>The paper showcases the performance of the proposed models in terms of accuracy and fairness.
  arXiv  Detail & Related papers  (2026-01-13T18:17:43Z)
• Fair Bayesian Data Selection via Generalized Discrepancy Measures [11.013077130984973]
  We propose a data selection framework that ensures fairness by aligning group-specific posterior distributions of model parameters and sample weights with a shared central distribution.<n>Our framework supports flexible alignment via various distributional discrepancy measures, including Wasserstein distance, maximum mean discrepancy, and $f$-divergence.<n> Experiments on benchmark datasets show that our method consistently outperforms existing data selection and model-based fairness methods in both fairness and accuracy.
  arXiv  Detail & Related papers  (2025-11-10T12:28:04Z)
• Nonparametric Data Attribution for Diffusion Models [57.820618036556084]
  Data attribution for generative models seeks to quantify the influence of individual training examples on model outputs.<n>We propose a nonparametric attribution method that operates entirely on data, measuring influence via patch-level similarity between generated and training images.
  arXiv  Detail & Related papers  (2025-10-16T03:37:16Z)
• Composition and Alignment of Diffusion Models using Constrained Learning [79.36736636241564]
  Diffusion models have become prevalent in generative modeling due to their ability to sample from complex distributions.<n>Two commonly used methods are: (i) alignment, which involves fine-tuning a diffusion model to align it with a reward; and (ii) composition, which combines several pre-trained diffusion models, each emphasizing a desirable attribute in the generated outputs.<n>We propose a constrained optimization framework that unifies alignment and composition of diffusion models by enforcing that the aligned model satisfies reward constraints and/or remains close to (potentially multiple) pre-trained models.
  arXiv  Detail & Related papers  (2025-08-26T15:06:30Z)
• General Post-Processing Framework for Fairness Adjustment of Machine Learning Models [0.0]
  This paper introduces a novel framework for fairness adjustments that applies to diverse machine learning tasks. By decoupling fairness adjustments from the model training process, our framework preserves model performance on average. We demonstrate the effectiveness of this approach by comparing it to Adversarial Debiasing, showing that our framework achieves a comparable fairness/accuracy tradeoff on real-world datasets.
  arXiv  Detail & Related papers  (2025-04-22T20:06:59Z)
• Multi-Output Distributional Fairness via Post-Processing [47.94071156898198]
  We introduce a post-processing method for multi-output models to enhance a model's distributional parity, a task-agnostic fairness measure.<n>Existing methods for achieving distributional parity rely on the (inverse) cumulative density function of a model's output.<n>An approximation technique is applied to reduce the complexity of computing the exact barycenter.
  arXiv  Detail & Related papers  (2024-08-31T22:41:26Z)
• A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization [7.378582040635655]
  Current deep learning approaches rely on generative models that yield exact sample likelihoods. This work introduces a method that lifts this restriction and opens the possibility to employ highly expressive latent variable models. We experimentally validate our approach in data-free Combinatorial Optimization and demonstrate that our method achieves a new state-of-the-art on a wide range of benchmark problems.
  arXiv  Detail & Related papers  (2024-06-03T17:55:02Z)
• Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models [54.132297393662654]
  We introduce a hybrid method that fine-tunes cutting-edge diffusion models by optimizing reward models through RL. We demonstrate the capability of our approach to outperform the best designs in offline data, leveraging the extrapolation capabilities of reward models.
  arXiv  Detail & Related papers  (2024-05-30T03:57:29Z)
• Integrating Fairness and Model Pruning Through Bi-level Optimization [16.213634992886384]
  We introduce a novel concept of fair model pruning, which involves developing a sparse model that adheres to fairness criteria.<n>In particular, we propose a framework to jointly optimize the pruning mask and weight update processes with fairness constraints.<n>This framework is engineered to compress models that maintain performance while ensuring fairness in a unified process.
  arXiv  Detail & Related papers  (2023-12-15T20:08:53Z)
• Ensemble Modeling for Multimodal Visual Action Recognition [50.38638300332429]
  We propose an ensemble modeling approach for multimodal action recognition. We independently train individual modality models using a variant of focal loss tailored to handle the long-tailed distribution of the MECCANO [21] dataset.
  arXiv  Detail & Related papers  (2023-08-10T08:43:20Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)
• Planning with Diffusion for Flexible Behavior Synthesis [125.24438991142573]
  We consider what it would look like to fold as much of the trajectory optimization pipeline as possible into the modeling problem. The core of our technical approach lies in a diffusion probabilistic model that plans by iteratively denoising trajectories.
  arXiv  Detail & Related papers  (2022-05-20T07:02:03Z)
• fairlib: A Unified Framework for Assessing and Improving Classification Fairness [66.27822109651757]
  fairlib is an open-source framework for assessing and improving classification fairness. We implement 14 debiasing methods, including pre-processing, at-training-time, and post-processing approaches. The built-in metrics cover the most commonly used fairness criterion and can be further generalized and customized for fairness evaluation.
  arXiv  Detail & Related papers  (2022-05-04T03:50:23Z)
• Deblurring via Stochastic Refinement [85.42730934561101]
  We present an alternative framework for blind deblurring based on conditional diffusion models. Our method is competitive in terms of distortion metrics such as PSNR.
  arXiv  Detail & Related papers  (2021-12-05T04:36:09Z)
• Group-Aware Threshold Adaptation for Fair Classification [9.496524884855557]
  We introduce a novel post-processing method to optimize over multiple fairness constraints. Our method theoretically enables a better upper bound in near optimality than existing method under same condition. Experimental results demonstrate that our method outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-08T04:36:37Z)
• Learning Diverse Representations for Fast Adaptation to Distribution Shift [78.83747601814669]
  We present a method for learning multiple models, incorporating an objective that pressures each to learn a distinct way to solve the task. We demonstrate our framework's ability to facilitate rapid adaptation to distribution shift.
  arXiv  Detail & Related papers  (2020-06-12T12:23:50Z)

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.