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	<!DOCTYPE html>
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	<meta charset="utf-8">
	<meta name="description"
	content="Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models">
	<meta name="keywords" content="Machine Learning, Bias Mitigation, Benchmark">
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	<title>BMBENCH: Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models</title>

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	<div class="container is-max-desktop">
	<div class="columns is-centered">
	<div class="column has-text-centered">
	<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>
	</div>

	<div class="is-size-5 publication-authors">
	<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>
	</div>

	<div class="column has-text-centered">
	<div class="publication-links">
	<!-- PDF Link. -->
	<span class="link-block">
	<a href="https://arxiv.org/pdf/2011.12948" target="_blank"
	class="external-link button is-normal is-rounded is-dark">
	<span class="icon">
	<i class="fas fa-file-pdf"></i>
	</span>
	<span>Paper</span>
	</a>
	</span>
	<span class="link-block">
	<a href="https://arxiv.org/abs/2011.12948" target="_blank"
	class="external-link button is-normal is-rounded is-dark">
	<span class="icon">
	<i class="ai ai-arxiv"></i>
	</span>
	<span>arXiv</span>
	</a>
	</span>
	<!-- Code Link. -->
	<span class="link-block">
	<a href="https://github.com/google/nerfies" target="_blank"
	class="external-link button is-normal is-rounded is-dark">
	<span class="icon">
	<i class="fab fa-github"></i>
	</span>
	<span>Code</span>
	</a>
	</span>
	<!-- Leaderboard. -->
	<span class="link-block">
	<a href="https://github.com/google/nerfies/releases/tag/0.1" target="_blank"
	class="external-link button is-normal is-rounded is-dark">
	<span class="icon">
	<i class="far fa-images"></i>
	</span>
	<span>Leaderboard</span>
	</a>
	</div>
	</div>
	</div>
	</div>
	</div>
	</div>
	</section>

	<section class="hero teaser">
	<div class="container is-max-desktop">
	<div class="hero-body">
	<img src="./static/images/bmbench.png" alt="BMBENCH Image" width="100%">
	<h2 class="subtitle has-text-centered">
	<span class="dnerf">BMBENCH</span> framework and pipeline process.
	</h2>
	</div>
	</div>
	</section>


	<section class="section">
	<div class="container is-max-desktop">
	<!-- Abstract. -->
	<div class="columns is-centered has-text-centered">
	<div class="column is-four-fifths">
	<h2 class="title is-3">Abstract</h2>
	<div class="content has-text-justified">
	<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.
	</p>
	</div>
	</div>
	</div>
	<!--/ Abstract. -->
	</section>


	<section class="section">
	<div class="container is-max-desktop">
	<div class="columns is-centered">
	<!-- Table and Filters Section -->
	<div class="column">
	<div class="content">
	<h2 class="title is-3">Filtered Dataset Table</h2>
	<p>
	Use the filters below to select a dataset view by task and stage. Each combination returns a filtered Hugging Face dataset.
	</p>
	<!-- Filters -->
	<div class="filters">
	<label for="task">Task:</label>
	<select id="task" onchange="updateTable()">
	<option value="binary_classification">Binary Classification</option>
	<!-- Add more task options if needed -->
	</select>
	<label for="stage">Stage:</label>
	<select id="stage" onchange="updateTable()">
	<option value="preprocessing">Preprocessing</option>
	<!-- Add more stage options if needed -->
	</select>
	</div>
	<!--/ Filters -->

	<!-- Table -->
	<table id="datasetTable" class="table is-striped">
	<thead>
	<tr id="tableHeader">
	<!-- Table headers will be dynamically populated -->
	</tr>
	</thead>
	<tbody id="tableBody">
	<!-- Table rows will be dynamically populated -->
	</tbody>
	</table>
	<!--/ Table -->
	</div>
	</div>
	</div>
	</div>
	</section>

	<script>
	async function fetchDataset(task, stage) {
	// Create URL based on the selected task and stage
	const datasetURL = `https://huggingface.co/datasets/holistic-ai/bias_mitigation_benchmark/resolve/main/benchmark_${task}_${stage}.csv`;

	const response = await fetch(datasetURL);
	const text = await response.text();

	// Parse CSV text into a format suitable for the table
	const data = Papa.parse(text, { header: true }); // Use PapaParse library for CSV parsing
	return data.data;
	}

	async function updateTable() {
	const task = document.getElementById('task').value;
	const stage = document.getElementById('stage').value;

	// Fetch dataset based on current filter values
	const dataset = await fetchDataset(task, stage);

	// Populate table headers
	const tableHeader = document.getElementById('tableHeader');
	tableHeader.innerHTML = ''; // Clear existing headers
	const headers = Object.keys(dataset[0] \|\| {});
	headers.forEach(column => {
	const th = document.createElement('th');
	th.textContent = column;
	tableHeader.appendChild(th);
	});

	// Populate table rows
	const tableBody = document.getElementById('tableBody');
	tableBody.innerHTML = ''; // Clear existing rows
	dataset.forEach(row => {
	const tr = document.createElement('tr');
	headers.forEach(column => {
	const td = document.createElement('td');
	td.textContent = row[column];
	tr.appendChild(td);
	});
	tableBody.appendChild(tr);
	});
	}
	</script>

	<script src="https://cdnjs.cloudflare.com/ajax/libs/PapaParse/5.3.0/papaparse.min.js"></script>



	<section class="section" id="BibTeX">
	<div class="container is-max-desktop content">
	<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>
	</div>
	</section>


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