Mahdi Zakizadeh — accepted AI-conference papers

2025

Blind Men and the Elephant: Diverse Perspectives on Gender Stereotypes in Benchmark Datasets

Mahdi Zakizadeh, Mohammad Taher Pilehvar

EMNLP 2025

Accurately measuring gender stereotypical bias in language models is a complex task with many hidden aspects. Current benchmarks have underestimated this multifaceted challenge and failed to capture the full extent of the problem. This paper examines the inconsistencies between intrinsic stereotype

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2023

DiFair: A Benchmark for Disentangled Assessment of Gender Knowledge and Bias

Mahdi Zakizadeh, Kaveh Eskandari Miandoab, Mohammad Taher Pilehvar

EMNLP 2023long findings

Numerous debiasing techniques have been proposed to mitigate the gender bias that is prevalent in pretrained language models. These are often evaluated on datasets that check the extent to which the model is gender-neutral in its predictions. Importantly, this evaluation protocol overlooks the poss…

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