xwhy/index.html at main · Dependable-Intelligent-Systems-Lab/xwhy · GitHub

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<!DOCTYPE html>
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  <title>SMILE: Statistical Model-agnostic Interpretability</title>
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    content="Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations">

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        <h1 class="site-title"><a href="index.html" style="color: inherit;">XWhy / SMILE</a></h1>
        <p class="site-subtitle">Explainable AI Research</p>
      </div>

      <ul class="nav-links">
        <li><a href="#about" class="active">About</a></li>
        <li><a href="#news">News</a></li>
        <li><a href="#publications">Publications</a></li>
        <li><a href="#team">Team</a></li>
        <li><a href="llm.html">LLM Explainability</a></li>
        <li><a href="geo.html">Geo-SMILE</a></li>
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        <a href="https://arxiv.org/abs/2311.07286" title="arXiv"><i class="ai ai-arxiv"></i></a>
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        &copy; 2025 XWhy Project.
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        </section>

        <!-- About Section -->
        <section id="about">
          <h1 class="page-title">Explaining black boxes with a SMILE</h1>
          <p class="lead">
            <strong>SMILE</strong> (Statistical Model-agnostic Interpretability with Local Explanations) is a new method
            that improves explainability by making use of statistical distance measures.
          </p>
          <p>
            Machine learning is currently undergoing an explosion in capability, popularity, and sophistication.
            However, one of the major barriers to widespread acceptance of machine learning (ML) is trustworthiness:
            most ML models operate as black boxes, their inner workings opaque and mysterious.
          </p>
          <p>
            Explainability is a key aspect of improving trustworthiness. SMILE builds on previous approaches to provide
            robust, model-agnostic explanations applicable to a wide range of input data domains.
          </p>
        </section>

        <!-- News Section -->
        <section id="news">
          <h2 class="section-title">News</h2>
          <table class="news-table">
            <tr>
              <td class="news-date">Jan 2024</td>
              <td>Paper published in <strong>IEEE Software</strong>.</td>
            </tr>
            <tr>
              <td class="news-date">Nov 2023</td>
              <td>Preprint available on <strong>arXiv</strong>.</td>
            </tr>
            <tr>
              <td class="news-date">2023</td>
              <td>SMILE method proposed for robust XAI.</td>
            </tr>
          </table>
        </section>

        <!-- Publications Section -->
        <section id="publications">
          <h2 class="section-title">Selected Publications</h2>

          <div class="publication-item">
            <div class="pub-thumbnail">
              <!-- Placeholder for paper thumbnail if available, using logo for now -->
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            <div class="pub-content">
              <a href="https://www.computer.org/csdl/magazine/so/2024/01/10272255/1QYrRultOo0"
                class="pub-title">Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with
                Local Explanations</a>
              <div class="pub-authors">
                Koorosh Aslansefat, Mojgan Hashemian, Martin Walker, Mohammed Naveed Akram, Ioannis Sorokos, Yiannis
                Papadopoulos
              </div>
              <div class="pub-venue">IEEE Software, 2023</div>
              <div class="pub-links">
                <a href="https://arxiv.org/abs/2311.07286">arXiv</a>
                <a href="https://www.computer.org/csdl/magazine/so/2024/01/10272255/1QYrRultOo0">PDF</a>
                <a href="https://github.com/Dependable-Intelligent-Systems-Lab/xwhy">Code</a>
              </div>
              <p style="font-size: 0.9rem; margin-top: 0.5rem;">
                We propose SMILE, a new method that builds on previous approaches by making use of statistical distance
                measures to improve explainability.
              </p>
            </div>
          </div>

        </section>

        <!-- Team Section -->
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          <h2 class="section-title">Team</h2>

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            <!-- Level 1: Lead -->
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              <div class="org-node">
                <img src="https://ui-avatars.com/api/?name=Koorosh+Aslansefat&background=random"
                  alt="Koorosh Aslansefat">
                <h4><a href="https://koo-ec.github.io/">Dr Koorosh Aslansefat</a></h4>
                <span class="org-role">Assistant Professor</span>
                <p>AI Safety and Explainability</p>
              </div>
            </div>

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                <!-- <img src="https://ui-avatars.com/api/?name=Kuniko+Paxton&background=random" alt="Kuniko Paxton"> -->
                <h4>Kuniko Paxton</h4>
                <span class="org-role">Fairness</span>
              </div>

              <div class="org-node node-teal">
                <!-- <img src="https://ui-avatars.com/api/?name=Zeinab+Dehghani&background=random" alt="Zeinab Dehghani"> -->
                <h4>Zeinab Dehghani</h4>
                <span class="org-role">GenAI Explainability</span>
              </div>

              <div class="org-node node-blue">
                <!-- <img src="https://ui-avatars.com/api/?name=Mostafa+Anoosha&background=random" alt="Mostafa Anoosha"> -->
                <h4>Mostafa Anoosha</h4>
                <span class="org-role">PGR Student</span>
                <p>Privacy-Preserving</p>
              </div>

              <div class="org-node node-green">
                <!-- <img src="https://ui-avatars.com/api/?name=Mohadeseh+Molapour&background=random" alt="Mohadeseh Molapour"> -->
                <h4>Mohadeseh Molapour</h4>
                <span class="org-role">GenAI Explainability</span>
              </div>

              <div class="org-node node-blue">
                <!-- <img src="https://ui-avatars.com/api/?name=Shadie+Mohammadi&background=random" alt="Shadie Mohammadi"> -->
                <h4>Shadie Mohammadi</h4>
                <span class="org-role">Geo-Explainability</span>
              </div>

              <div class="org-node node-purple">
                <!-- <img src="https://ui-avatars.com/api/?name=Louis+Donaldson&background=random" alt="Louis Donaldson"> -->
                <h4>Louis Donaldson</h4>
                <span class="org-role">RL Explainability</span>
              </div>

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