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+<a href="https://doi.org/10.48550/arXiv.2406.09069">On the Robustness of Global Feature Effect Explanations</a>
+<p>Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek</p>
+<p><strong>ECML PKDD (2024)</strong></p>
+We introduce several theoretical bounds for evaluating the robustness of partial dependence plots and accumulated local effects. Our experimental results with synthetic and real-world datasets quantify the gap between the best and worst-case scenarios of (mis)interpreting machine learning predictions globally.
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 <img src="images/redteaming_sam.png">
 <a href="https://openaccess.thecvf.com/content/CVPR2024W/AdvML/html/Jankowski_Red-Teaming_Segment_Anything_Model_CVPRW_2024_paper.html">Red-Teaming Segment Anything Model</a>
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 The Segment Anything Model is one of the first and most well-known foundation models for computer vision segmentation tasks. This work presents a multi-faceted red-teaming analysis of SAM. We analyze the impact of style transfer on segmentation masks. We assess whether the model can be used for attacks on privacy, such as recognizing celebrities' faces. Finally, we check how robust the model is to adversarial attacks on segmentation masks under text prompts.
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 <img src="images/redteaming_hsi.png">
 <a href="https://doi.org/10.48550/arXiv.2403.08017">Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI</a>