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<h1 class="title is-1 publication-title">BMBENCH: Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://kleytondacosta.com" target="_blank">Kleyton da Costa</a><sup>1, 2</sup>,</span>
<span class="author-block">
<a href="https://utkarshsinha.com" target="_blank">Cristian Munoz</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://jonbarron.info" target="_blank">Bernardo Modenesi</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="http://sofienbouaziz.com" target="_blank">Franklin Fernandez</a><sup>1,2</sup>,
</span>
<span class="author-block">
<a href="https://www.danbgoldman.com" target="_blank">Adriano Koshiyama</a><sup>1</sup>,
</span>
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<span class="author-block"><sup>1</sup>Holistic AI,</span>
<span class="author-block"><sup>2</sup>Pontifical Catholic University of Rio de Janeiro,</span>
<span class="author-block"><sup>2</sup>University of Utah,</span>
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<span class="dnerf">BMBENCH</span> framework and pipeline process.
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<p>
The development and assessment of bias mitigation methods require rigorous benchmarks.
This paper introduces BMBench, a comprehensive benchmarking framework to evaluate bias
mitigation strategies across multitask machine learning predictions (binary classification,
multiclass classification, regression, and clustering). Our benchmark leverages state-of-the-art
and proposed datasets to improve fairness research, offering a broad spectrum of fairness
metrics for a robust evaluation of bias mitigation methods. We provide an open-source repository
to allow researchers to test and refine their bias mitigation approaches easily,
promoting advancements in the creation of fair machine learning models.
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<h2 class="title">BibTeX</h2>
<pre><code>@article{dacosta2025bmbench,
author = {da Costa, K., Munoz, C., Modenesi, B., Fernandez, F., Koshiyama, A.},
title = {BMBENCH: Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models},
journal = {ICCV},
year = {2025},
}</code></pre>
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