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@@ -6,7 +6,7 @@
         content="Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models">
   <meta name="keywords" content="Machine Learning, Bias Mitigation, Benchmark">
   <meta name="viewport" content="width=device-width, initial-scale=1">
-  <title>BMBENCH: Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models</title>
+  <title>Evaluating Explainability in Machine Learning Predictions through Explainer-Agnostic Metrics</title>
 
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         rel="stylesheet">
@@ -33,17 +33,14 @@
     <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>
+          <h1 class="title is-1 publication-title">Evaluating Explainability in Machine Learning Predictions through Explainer-Agnostic Metrics</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://crismunoz.github.io/" target="_blank">Cristian Munoz</a><sup>1</sup>,</span>
             <span class="author-block">
-              <a href="https://sites.google.com/view/bmodenesi" target="_blank">Bernardo Modenesi</a><sup>3</sup>,
-            </span>
+              <a href="https://kleytondacosta.com" target="_blank">Kleyton da Costa</a><sup>1, 2</sup>,</span>
             <span class="author-block">
-              <a href="https://www.linkedin.com/in/franklin-cardenoso-fernandez/" target="_blank">Franklin Fernandez</a><sup>1,2</sup>,
+              <a href="https://sites.google.com/view/bmodenesi" target="_blank">Bernardo Modenesi</a><sup>3</sup>,
             </span>
             <span class="author-block">
               <a href="https://scholar.google.com/citations?user=MuJGqNAAAAAJ&hl=en" target="_blank">Adriano Koshiyama</a><sup>1</sup>
@@ -100,7 +97,7 @@
     <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.
+        <span class="dnerf">EAMEX</span> framework and pipeline process.
       </h2>
     </div>
   </div>
@@ -115,14 +112,17 @@
         <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.
+            The rapid integration of artificial intelligence (AI) into various industries has introduced new challenges in 
+            governance and regulation, particularly regarding the understanding of complex AI systems. A critical demand 
+            from decision-makers is the ability to explain the results of machine learning models, which is essential for 
+            fostering trust and ensuring ethical AI practices. In this paper, we develop six distinct model-agnostic metrics 
+            designed to quantify the extent to which model predictions can be explained. These metrics measure different aspects
+            of model explainability, ranging from local importance, global importance, and surrogate predictions, allowing for a 
+            comprehensive evaluation of how models generate their outputs. Furthermore, by computing our metrics, we can rank 
+            models in terms of explainability criteria such as importance concentration and consistency, prediction fluctuation, 
+            and surrogate fidelity and stability, offering a valuable tool for selecting models based not only on accuracy but 
+            also on transparency. We demonstrate the practical utility of these metrics on classification and regression tasks, 
+            and integrate these metrics into an existing Python package for public use.
           </p>
         </div>
       </div>
@@ -131,112 +131,13 @@
 </section>
 
 
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-<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/css/bulma.min.css">
-
-<script src="https://cdn.jsdelivr.net/npm/[email protected]/papaparse.min.js"></script>
-
-<div class="container">
-  <h1 class="title is-1 leaderboard-title">Leaderboard</h1>
-  <div class="field is-grouped is-grouped-multiline">
-    <div class="control">
-      <div class="select">
-        <select id="task-select">
-          <option value="binary_classification">Binary Classification</option>
-          <option value="multiclass">Multiclass Classification</option>
-          <option value="regression">Regression</option>
-          <option value="clustering">Clustering</option>
-        </select>
-      </div>
-    </div>
-    <div class="control">
-      <div class="select">
-        <select id="stage-select">
-          <option value="preprocessing">Preprocessing</option>
-          <option value="inprocessing">Inprocessing</option>
-          <option value="postprocessing">Postprocessing</option>
-        </select>
-      </div>
-    </div>
-  </div>
-
-  <div id="table-container"></div>
-</div>
-
-<script>
-function loadTable() {
-  const taskSelect = document.getElementById('task-select');
-  const stageSelect = document.getElementById('stage-select');
-  const task = taskSelect.value;
-  const stage = stageSelect.value;
-  const csvUrl = `https://huggingface.co/datasets/holistic-ai/bias_mitigation_benchmark/resolve/main/benchmark_${task}_${stage}.csv`;
-
-  // Clear previous table
-  document.getElementById('table-container').innerHTML = '';
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-      Papa.parse(csvData, {
-        header: true,
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-          const data = results.data;
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-
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-document.getElementById('stage-select').addEventListener('change', loadTable);
-</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},
-  year      = {2025},
+  author    = {Munoz, C., da Costa, K., Modenesi, B., Koshiyama, A.},
+  title     = {Evaluating Explainability in Machine Learning Predictions through Explainer-Agnostic Metrics},
+  year      = {2024},
   url       = {}
 }</code></pre>
   </div>