2026· International Conference on Data Technologies and Applications· pp. 1155-1162· 0 citations· 32 references
Computer Science
TL;DR
A robust, reliable, and reproducible methodology for evaluating group fairness in classification algorithms, and provides a methodological approach for assessing fairness within the Sufficiency criterion by operationalizing Calibration.
Abstract
: This paper proposes a robust, reliable, and reproducible methodology for evaluating group fairness in classification algorithms. Building upon established theoretical definitions of non-discrimination criteria - Independence, Separation, and Sufficiency-we present a comprehensive approach to quantifying fairness. Notably, our methodology extends beyond binary group comparisons to accommodate scenarios with multiple sensitive groups. Furthermore, we provide a methodological approach for assessing fairness within the Sufficiency criterion by operationalizing Calibration, elucidating critical issues and conceptual subtleties that appear to have been overlooked in existing literature. We assess the reliability and robustness of our model through applications to one real dataset.
Abstract Background Fairness evaluation is essential for trustworthy clinical risk prediction. However, existing fairness-oriented discrimination metrics either ignore cross-group comparisons or rely on exhaustive pairwise evaluations, making them difficult to interpret and impractical for model selection. Objective Th...
Hao-Yuan Wang, Chuan Hong, Michael J. Pencina et al.· JMIR AI· 0 citations
This work introduces a method to lower-bound the discrepancy of a classifier: a quantity that jointly captures inaccuracy and unfairness, and develops a computationally efficient procedure for calculating the tightest possible lower bound on the classifier’s discrepancy.
This article proposes that algorithmic fairness is best understood as a human-technology interaction problem rather than a purely technical challenge, and offers an interdisciplinary perspective that integrates insights from social justice, psychology, computer science, judgment and decision-making, and management.
A. Lemmens· Current Opinion in Psycholog...· 0 citations
Neighborhood-based fairness audits evaluate individual fairness by comparing predictions among similar individuals in feature space. Despite their widespread use, little is known about the robustness of the auditing procedure itself. Because these audits rely on nearest neighbor relationships, small perturbations in fe...
The empirical findings support the use of complementary structural and policy-level interventions and demonstrate the importance of jointly evaluating aggregate disparity, worst-case attribute-level harm, cross-attribute transfer, and predictive utility.
A thorough literature review is provided to encapsulate prior research on bias identification and fairness auditing, categorizing the findings according to various stages of study and proposing a unified pipeline for dataset integration and a modular framework for bias auditing.
Nani Kartik Kaveti, T. Pattanshetti· Discover Artificial Intellig...· 0 citations
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