Uncovering Fairness through Data Complexity as an Early Indicator

• URL: http://arxiv.org/abs/2504.05923v1
• Date: Tue, 08 Apr 2025 11:28:40 GMT
• Title: Uncovering Fairness through Data Complexity as an Early Indicator
• Authors: Juliett Suárez Ferreira, Marija Slavkovik, Jorge Casillas,
• Abstract summary: There is no study on how disparities in classification complexity between privileged and unprivileged groups could influence the fairness of solutions.<n>In this work, we focus on synthetic datasets designed to capture a variety of biases ranging from historical bias to measurement and representational bias.<n>We then apply association rule mining to identify patterns that link disproportionate complexity differences between groups with fairness-related outcomes.
• Score: 0.7783262415147651
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Fairness constitutes a concern within machine learning (ML) applications. Currently, there is no study on how disparities in classification complexity between privileged and unprivileged groups could influence the fairness of solutions, which serves as a preliminary indicator of potential unfairness. In this work, we investigate this gap, specifically, we focus on synthetic datasets designed to capture a variety of biases ranging from historical bias to measurement and representational bias to evaluate how various complexity metrics differences correlate with group fairness metrics. We then apply association rule mining to identify patterns that link disproportionate complexity differences between groups with fairness-related outcomes, offering data-centric indicators to guide bias mitigation. Our findings are also validated by their application in real-world problems, providing evidence that quantifying group-wise classification complexity can uncover early indicators of potential fairness challenges. This investigation helps practitioners to proactively address bias in classification tasks.

Related papers

• Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors [28.869581543676947]
  unsupervised outlier detection (OD) has numerous applications in finance, security, etc. This work aims to shed light on the possible sources of unfairness in OD by auditing detection models under different data-centric factors. We find that the OD algorithms under the study all exhibit fairness pitfalls, although differing in which types of data bias they are more susceptible to.
  arXiv  Detail & Related papers  (2024-08-24T20:35:32Z)
• Correcting Underrepresentation and Intersectional Bias for Classification [49.1574468325115]
  We consider the problem of learning from data corrupted by underrepresentation bias. We show that with a small amount of unbiased data, we can efficiently estimate the group-wise drop-out rates. We show that our algorithm permits efficient learning for model classes of finite VC dimension.
  arXiv  Detail & Related papers  (2023-06-19T18:25:44Z)
• 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)
• Fair Correlation Clustering in Forests [8.810926150873991]
  A clustering is said to be fair, if each cluster has the same distribution of manifestations of a sensitive attribute as the whole input set. This is motivated by various applications where the objects to be clustered have sensitive attributes that should not be over- or underrepresented. We consider restricted graph classes which allow us to characterize the distributions of sensitive attributes for which this form of fairness is tractable.
  arXiv  Detail & Related papers  (2023-02-22T11:27:06Z)
• Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification [31.392067805022414]
  Variance in predictions across different trained models is a significant, under-explored source of error in fair binary classification. In practice, the variance on some data examples is so large that decisions can be effectively arbitrary. We develop an ensembling algorithm that abstains from classification when a prediction would be arbitrary.
  arXiv  Detail & Related papers  (2023-01-27T06:52:04Z)
• Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions [4.159343412286401]
  We argue that algorithmic unfairness stems from interactions between models and biases in the data. We study a set of hypotheses regarding the fairness-accuracy trade-offs that fairness-blind ML algorithms exhibit under different data bias settings.
  arXiv  Detail & Related papers  (2022-07-13T15:18:30Z)
• 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)
• Fair Group-Shared Representations with Normalizing Flows [68.29997072804537]
  We develop a fair representation learning algorithm which is able to map individuals belonging to different groups in a single group. We show experimentally that our methodology is competitive with other fair representation learning algorithms.
  arXiv  Detail & Related papers  (2022-01-17T10:49:49Z)
• 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)
• MultiFair: Multi-Group Fairness in Machine Learning [52.24956510371455]
  We study multi-group fairness in machine learning (MultiFair) We propose a generic end-to-end algorithmic framework to solve it. Our proposed framework is generalizable to many different settings.
  arXiv  Detail & Related papers  (2021-05-24T02:30:22Z)
• Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification [75.49600684537117]
  Data management research is showing an increasing presence and interest in topics related to data and algorithmic fairness. We contribute a broad analysis of 13 fair classification approaches and additional variants, over their correctness, fairness, efficiency, scalability, and stability. Our analysis highlights novel insights on the impact of different metrics and high-level approach characteristics on different aspects of performance.
  arXiv  Detail & Related papers  (2021-01-18T22:55:40Z)
• Fair Classification with Group-Dependent Label Noise [6.324366770332667]
  This work examines how to train fair classifiers in settings where training labels are corrupted with random noise. We show that naively imposing parity constraints on demographic disparity measures, without accounting for heterogeneous and group-dependent error rates, can decrease both the accuracy and the fairness of the resulting classifier.
  arXiv  Detail & Related papers  (2020-10-31T22:35:01Z)

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.