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</style><p class="MsoNormal" style="font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">We invite researchers working on <b>interpretability and explainability in machine learning and artificial intelligence</b>, as well as related topics, to submit <b>regular papers (14 pages, single column)</b> or <b>short papers (7 pages, single column)</b>to the <b>AIMLAI Workshop</b>, to be held in conjunction with <b>ECML/PKDD 2025</b>.</span><span style="font-size: 13.5pt;"><o:p></o:p></span></p><p class="MsoNormal" style="font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><b><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Website:</span></b><span style="font-size: 11pt; font-family: Calibri, sans-serif;"> </span><a href="https://project.inria.fr/aimlai/" target="_new"><span style="font-size: 11pt; font-family: Calibri, sans-serif; color: blue;">https://project.inria.fr/aimlai/</span></a><span style="font-size: 11pt; font-family: Calibri, sans-serif;"><br><b>Submission link:</b> </span><a href="https://cmt3.research.microsoft.com/ECMLPKDDWorkshopTrack2025/Submission/Index"><span style="font-size: 13.5pt; color: blue;">https://cmt3.research.microsoft.com/ECMLPKDDWorkshopTrack2025/Submission/Index</span></a><span style="font-size: 11pt; font-family: Calibri, sans-serif;"><br><b>Submission deadline:</b> <b>June 14, 2025</b></span><span style="font-size: 13.5pt;"><o:p></o:p></span></p><p class="MsoNormal" style="font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><span style="font-size: 13.5pt;"> </span></p><p class="MsoNormal" style="font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><b><span style="font-size: 11pt; font-family: Calibri, sans-serif;">AIMLAI (Advances in Interpretable Machine Learning and Artificial Intelligence)</span></b><span style="font-size: 11pt; font-family: Calibri, sans-serif;"> aims to foster principled research and discussion on building explainable machine learning and AI systems . We invite contributions from researchers in academia and industry that approach the challenges of achieving explainability and interpretability in AI systems from <b>technical</b>, as well as <b>legal, ethical, or sociological perspectives</b>.</span><span style="font-size: 13.5pt;"><o:p></o:p></span></p><p class="MsoNormal" style="font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><span style="font-size: 13.5pt;"> </span></p><p class="MsoNormal" style="font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Submissions may include:</span><span style="font-size: 13.5pt;"><o:p></o:p></span></p><ul type="disc" style="margin-bottom: 0cm; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); margin-top: 0cm;"><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Novel research results</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Application experiences</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Tools and systems</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Preliminary ideas with promising potential</span><o:p></o:p></li></ul><p class="MsoNormal" style="font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><b><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Topics of Interest (non-exhaustive)</span></b><span style="font-size: 13.5pt;"><o:p></o:p></span></p><p class="MsoNormal" style="font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><span style="font-size: 13.5pt;"> </span></p><p class="MsoNormal" style="font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><b><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Interpretable Machine Learning</span></b><span style="font-size: 13.5pt;"><o:p></o:p></span></p><ul type="disc" style="margin-bottom: 0cm; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); margin-top: 0cm;"><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Interpretable-by-design models</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Explainable recommendation systems</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Multimodal explanations</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Explainability for large language models (LLMs)</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">LLMs as tools for explainability</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Mechanistic interpretability</span><o:p></o:p></li></ul><p class="MsoNormal" style="margin-left: 36pt; font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><o:p> </o:p></p><p class="MsoNormal" style="font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><b><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Transparency, Ethics, Fairness and Privacy</span></b><span style="font-size: 13.5pt;"><o:p></o:p></span></p><ul type="disc" style="margin-bottom: 0cm; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); margin-top: 0cm;"><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Ethical implications of AI/ML</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Legal frameworks and compliance</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Fairness and bias mitigation</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Interplay of explainability and privacy </span><o:p></o:p></li></ul><p class="MsoNormal" style="margin-left: 36pt; font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><o:p> </o:p></p><p class="MsoNormal" style="font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><b><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Methodology & Evaluation</span></b><span style="font-size: 13.5pt;"><o:p></o:p></span></p><ul type="disc" style="margin-bottom: 0cm; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); margin-top: 0cm;"><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Formal measures of interpretability and explainability</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Trade-offs between interpretability and model complexity</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Evaluation frameworks</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">User-centered interpretability</span><o:p></o:p></li></ul><p class="MsoNormal" style="font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><b><span style="font-size: 11pt; font-family: Calibri, sans-serif;"> Explanation Modules & Human Integration</span></b><span style="font-size: 13.5pt;"><o:p></o:p></span></p><ul type="disc" style="margin-bottom: 0cm; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); margin-top: 0cm;"><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Semantics in explanations</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Human-in-the-loop systems</span><o:p></o:p></li><li class="MsoNormal"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">Combining ML, information visualization, and human-computer interaction</span><o:p></o:p></li></ul><p class="MsoNormal" style="font-size: medium; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><span style="font-size: 11pt; font-family: Calibri, sans-serif;">We look forward to your contributions and to engaging discussions at AIMLAI 2025!</span><span style="font-size: 13.5pt;"><o:p></o:p></span></p></body></html>