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<title>Evaluating Explainability in Machine Learning Predictions through Explainer-Agnostic Metrics</title> |
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<h1 class="title is-1 publication-title">Evaluating Explainability in Machine Learning Predictions through Explainer-Agnostic Metrics</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://crismunoz.github.io/" target="_blank">Cristian Munoz</a><sup>1</sup>,</span> |
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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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<a href="https://sites.google.com/view/bmodenesi" target="_blank">Bernardo Modenesi</a><sup>3</sup>, |
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<span class="author-block"> |
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<a href="https://scholar.google.com/citations?user=MuJGqNAAAAAJ&hl=en" 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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<span>Paper</span> |
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<a href="https://arxiv.org/abs/2011.12948" target="_blank" |
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<span>arXiv</span> |
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<a href="https://github.com/holistic-ai/holisticai-research/tree/main/bmbench" target="_blank" |
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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">EAMEX</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 rapid integration of artificial intelligence (AI) into various industries has introduced new challenges in |
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governance and regulation, particularly regarding the understanding of complex AI systems. A critical demand |
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from decision-makers is the ability to explain the results of machine learning models, which is essential for |
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fostering trust and ensuring ethical AI practices. In this paper, we develop six distinct model-agnostic metrics |
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designed to quantify the extent to which model predictions can be explained. These metrics measure different aspects |
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of model explainability, ranging from local importance, global importance, and surrogate predictions, allowing for a |
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comprehensive evaluation of how models generate their outputs. Furthermore, by computing our metrics, we can rank |
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models in terms of explainability criteria such as importance concentration and consistency, prediction fluctuation, |
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and surrogate fidelity and stability, offering a valuable tool for selecting models based not only on accuracy but |
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also on transparency. We demonstrate the practical utility of these metrics on classification and regression tasks, |
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and integrate these metrics into an existing Python package for public use. |
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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 = {Munoz, C., da Costa, K., Modenesi, B., Koshiyama, A.}, |
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title = {Evaluating Explainability in Machine Learning Predictions through Explainer-Agnostic Metrics}, |
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year = {2024}, |
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url = {} |
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}</code></pre> |
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