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content="Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models"> |
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<title>BMBENCH: Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models</title> |
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<h1 class="title is-1 publication-title">BMBENCH: Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models</h1> |
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<div class="is-size-5 publication-authors"> |
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<span class="author-block"> |
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<a href="https://kleytondacosta.com" target="_blank">Kleyton da Costa</a><sup>1, 2</sup>,</span> |
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<span class="author-block"> |
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<a href="https://utkarshsinha.com" target="_blank">Cristian Munoz</a><sup>1</sup>,</span> |
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<span class="author-block"> |
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<a href="https://jonbarron.info" target="_blank">Bernando Modenesi</a><sup>3</sup>, |
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</span> |
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<span class="author-block"> |
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<a href="http://sofienbouaziz.com" target="_blank">Franklin Fernandez</a><sup>1,2</sup>, |
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<span class="author-block"> |
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<a href="https://www.danbgoldman.com" target="_blank">Adriano Koshiyama</a><sup>1</sup>, |
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<div class="is-size-5 publication-authors"> |
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<span class="author-block"><sup>1</sup>Holistic AI,</span> |
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<span class="author-block"><sup>2</sup>Pontifical Catholic University of Rio de Janeiro,</span> |
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<span class="author-block"><sup>2</sup>University of Utah,</span> |
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<div class="publication-links"> |
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<a href="https://arxiv.org/pdf/2011.12948" target="_blank" |
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class="external-link button is-normal is-rounded is-dark"> |
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<i class="fas fa-file-pdf"></i> |
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<span>Paper</span> |
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</a> |
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<a href="https://arxiv.org/abs/2011.12948" target="_blank" |
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class="external-link button is-normal is-rounded is-dark"> |
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<i class="ai ai-arxiv"></i> |
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<span>arXiv</span> |
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<a href="https://github.com/google/nerfies" target="_blank" |
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<span>Code</span> |
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<a href="https://github.com/google/nerfies/releases/tag/0.1" target="_blank" |
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<span>Leaderboard</span> |
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<section class="hero teaser"> |
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<img src="./static/images/bmbench.png" alt="BMBENCH Image" width="100%"> |
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<h2 class="subtitle has-text-centered"> |
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<span class="dnerf">BMBENCH</span> framework and pipeline process. |
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<h2 class="title is-3">Abstract</h2> |
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<p> |
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The development and assessment of bias mitigation methods require rigorous benchmarks. |
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This paper introduces BMBench, a comprehensive benchmarking framework to evaluate bias |
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mitigation strategies across multitask machine learning predictions (binary classification, |
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multiclass classification, regression, and clustering). Our benchmark leverages state-of-the-art |
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and proposed datasets to improve fairness research, offering a broad spectrum of fairness |
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metrics for a robust evaluation of bias mitigation methods. We provide an open-source repository |
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to allow researchers to test and refine their bias mitigation approaches easily, |
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promoting advancements in the creation of fair machine learning models. |
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</p> |
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<h2 class="title is-3">Visual Effects</h2> |
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Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect |
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would be impossible without nerfies since it would require going through a wall. |
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<video id="dollyzoom" autoplay controls muted loop playsinline height="100%"> |
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<source src="./static/videos/dollyzoom-stacked.mp4" |
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<section class="section" id="BibTeX"> |
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<h2 class="title">BibTeX</h2> |
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<pre><code>@article{dacosta2025bmbench, |
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author = {da Costa, K., Munoz, C., Modenesi, B., Fernandez, F., Koshiyama, A.}, |
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title = {BMBENCH: Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models}, |
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journal = {ICCV}, |
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year = {2025}, |
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}</code></pre> |
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