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.
This study develops and evaluates novel fairness-oriented discrimination metrics for clinical risk prediction that address limitations of within-group and pairwise cross-group approaches, and proposes group-level extensions of the CI and AUC that summarize subgroup-specific performance in a single interpretable measure...
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
This work develops a geometric framework for analyzing the robustness of neighborhood-based fairness audits under bounded perturbations, establishes sufficient conditions for neighborhood invariance, quantifies how neighborhood replacement propagates to audit instability, and introduces audit volatility, a measure of t...
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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