A reproduces three of Kamiran and Calders' empirical findings and extends the work by investigating how ranker choice affects the performance of Massaging and Preferential Sampling, recommending Massaging as the most reliable technique for practical deployment.
Results indicate that global feature importance, used as an active search signal rather than a post-hoc diagnostic, improves both the effectiveness and the efficiency of individual fairness testing.
H. Mamman, Abdullateef Oluwagbemiga Balogun, Mustapha Maidawa et al.· Journal of King Saud Univers...· 0 citations
It is shown that racial bias is not only present in the COMPAS dataset but is also amplified by the models trained on it, which undercuts both the assumption that algorithmic decision-making offers a neutral improvement over human judgment and the weaker claim that it merely mirrors preexisting human bias.
Ignacio Cofone, Warut Khern-am-nuai University of Oxford, M. University· 3 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.
FairFund-Bench is introduced, a benchmark that systematically varies key features of previous audit designs: the evaluation task (rating, ranking, or allocation), comparison context (single or multi-stimulus), and whether the audit is transparent or disguised, indicating that current LLMs robustly reproduce human deser...
Findings indicate that state-of-the-art LLMs can achieve a high degree of demographic neutrality; fundamental artefacts such as positional bias can nonetheless produce severely discriminatory outcomes; and bias auditing must extend beyond demographic parity to interaction artefacts and ecosystem structure.
A. Camargo, Rafaela Silva Figueiredo Camargo· Revista de Geopolítica· 0 citations
An integrated framework for bias detection and mitigation in AI-driven recruitment systems, combining interventions at the data, model, and decision levels within a unified evaluation pipeline is introduced, demonstrating that bias in recruitment systems arises from the interplay between data and model dynamics.
Gideon Assafuah, Claude Turner, C. Turner et al.· International Journal of Adv...· 0 citations
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