FairTree: Subgroup Fairness Auditing of Machine Learning Models with Bias-Variance Decomposition

• URL: http://arxiv.org/abs/2604.19357v1
• Date: Tue, 21 Apr 2026 11:38:59 GMT
• Title: FairTree: Subgroup Fairness Auditing of Machine Learning Models with Bias-Variance Decomposition
• Abstract summary: We introduce FairTree, a novel algorithm adapted from psychometric invariance testing.<n>It handles continuous, categorical, and ordinal features without discretization.<n>It decomposes performance disparities into systematic bias and variance, allowing a categorization of changes in algorithm performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The evaluation of machine learning models typically relies mainly on performance metrics based on loss functions, which risk to overlook changes in performance in relevant subgroups. Auditing tools such as SliceFinder and SliceLine were proposed to detect such groups, but usually have conceptual disadvantages, such as the inability to directly address continuous covariates. In this paper, we introduce FairTree, a novel algorithm adapted from psychometric invariance testing. Unlike SliceFinder and related algorithms, FairTree directly handles continuous, categorical, and ordinal features without discretization. It further decomposes performance disparities into systematic bias and variance, allowing a categorization of changes in algorithm performance. We propose and evaluate two variations of the algorithm: a permutation-based approach, which is conceptually closer to SliceFinder, and a fluctuation test. Through simulation studies that include a direct comparison with SliceLine, we demonstrate that both approaches have a satisfactory rate of false-positive results, but that the fluctuation approach has relatively higher power. We further illustrate the method on the UCI Adult Census dataset. The proposed algorithms provide a flexible framework for the statistical evaluation of the performance and aspects of fairness of machine learning models in a wide range of applications even in relatively small data.

Related papers

• An Enhanced Projection Pursuit Tree Classifier with Visual Methods for Assessing Algorithmic Improvements [0.3610948254980783]
  We develop visual diagnostic approaches to verify that the enhancements perform as intended.<n>An interactive web application enables users to explore the behavior of both the original and enhanced classifiers under controlled scenarios.
  arXiv  Detail & Related papers  (2026-02-24T17:27:17Z)
• Dynamic Classification: Leveraging Self-Supervised Classification to Enhance Prediction Performance [2.2736104746143355]
  We propose an innovative dynamic classification algorithm aimed at achieving zero missed detections and minimal false positives.<n>The algorithm partitions data in a self-supervised learning-generated way, which allows the model to learn from the training set.<n> Experimental results show that, with minimal data partitioning errors, the algorithm achieves exceptional performance.
  arXiv  Detail & Related papers  (2025-02-26T07:11:12Z)
• RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms [0.393259574660092]
  RHiOTS is designed to assess the robustness of hierarchical time series forecasting models and algorithms on real-world datasets. RHiOTS incorporates an innovative visualization component, turning complex, multidimensional robustness evaluation results into intuitive, easily interpretable visuals. Our findings show that traditional statistical methods are more robust than state-of-the-art deep learning algorithms, except when the transformation effect is highly disruptive.
  arXiv  Detail & Related papers  (2024-08-06T18:52:15Z)
• Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment [54.179859639868646]
  We propose a model agnostic post-processing framework xOrder for achieving fairness in bipartite ranking. xOrder is compatible with various classification models and ranking fairness metrics, including supervised and unsupervised fairness metrics. We evaluate our proposed algorithm on four benchmark data sets and two real-world patient electronic health record repositories.
  arXiv  Detail & Related papers  (2023-07-27T07:42:44Z)
• Towards Diverse Evaluation of Class Incremental Learning: A Representation Learning Perspective [67.45111837188685]
  Class incremental learning (CIL) algorithms aim to continually learn new object classes from incrementally arriving data. We experimentally analyze neural network models trained by CIL algorithms using various evaluation protocols in representation learning.
  arXiv  Detail & Related papers  (2022-06-16T11:44:11Z)
• Scalable Personalised Item Ranking through Parametric Density Estimation [53.44830012414444]
  Learning from implicit feedback is challenging because of the difficult nature of the one-class problem. Most conventional methods use a pairwise ranking approach and negative samplers to cope with the one-class problem. We propose a learning-to-rank approach, which achieves convergence speed comparable to the pointwise counterpart.
  arXiv  Detail & Related papers  (2021-05-11T03:38:16Z)
• Can Active Learning Preemptively Mitigate Fairness Issues? [66.84854430781097]
  dataset bias is one of the prevailing causes of unfairness in machine learning. We study whether models trained with uncertainty-based ALs are fairer in their decisions with respect to a protected class. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
  arXiv  Detail & Related papers  (2021-04-14T14:20:22Z)
• Greedy Search Algorithms for Unsupervised Variable Selection: A Comparative Study [3.4888132404740797]
  This paper focuses on unsupervised variable selection based dimensionality reduction. We present a critical evaluation of seven unsupervised greedy variable selection algorithms. We introduce and evaluate for the first time, a lazy implementation of the variance explained based forward selection component analysis (FSCA) algorithm.
  arXiv  Detail & Related papers  (2021-03-03T21:10:26Z)
• Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View [82.80085730891126]
  We provide the first modernally precise analysis of linear multiclass classification. Our analysis reveals that the classification accuracy is highly distribution-dependent. The insights gained may pave the way for a precise understanding of other classification algorithms.
  arXiv  Detail & Related papers  (2020-11-16T05:17:29Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)

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.