@inproceedings{AmlingScheeleSlanyetal., author = {Amling, Jonas and Scheele, Stephan and Slany, Emanuel and Lang, Moritz and Schmid, Ute}, title = {Explainable AI for Mixed Data Clustering}, series = {Explainable AI for Mixed Data Clustering:Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part II}, booktitle = {Explainable AI for Mixed Data Clustering:Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part II}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {9783031637964}, issn = {1865-0929}, doi = {10.1007/978-3-031-63797-1_3}, pages = {42 -- 62}, abstract = {Clustering, an unsupervised machine learning approach, aims to find groups of similar instances. Mixed data clustering is of particular interest since real-life data often consists of diverse data types. The unsupervised nature of clustering emphasizes the need to understand the criteria for defining and distinguishing clusters. Current explainable AI (XAI) methods for clustering focus on intrinsically explainable clustering techniques, surrogate model-based explanations utilizing established XAI frameworks, and explanations generated from inter-instance distances. However, there exists a research gap in developing post-hoc methods that directly explain clusterings without resorting to surrogate models or requiring prior knowledge about the clustering algorithm. Addressing this gap, our work introduces a model-agnostic, entropy-based Feature Importance Score for continuous and discrete data, offering direct and comprehensible explanations by highlighting key features, deriving rules, and identifying cluster prototypes. The comparison with existing XAI frameworks like SHAP and ClAMP shows that we achieve similar fidelity and simplicity, proving that mixed data clusterings can be effectively explained solely from the distributions of the features and assigned clusters, making complex clusterings comprehensible to humans.}, language = {en} } @unpublished{FrummetSlanyAmlingetal., author = {Frummet, Alexander and Slany, Emanuel and Amling, Jonas and Lang, Moritz and Scheele, Stephan}, title = {Explainable information retrieval in the audit domain}, doi = {10.48550/arXiv.2507.03479}, pages = {4}, abstract = {Conversational agents such as Microsoft Copilot and Google Gemini assist users with complex search tasks but often generate misleading or fabricated references. This undermines trust, particularly in high-stakes domains such as medicine and finance. Explainable information retrieval (XIR) aims to address this by making search results more transparent and interpretable. While most XIR research is domain-agnostic, this paper focuses on auditing -- a critical yet underexplored area. We argue that XIR systems can support auditors in completing their complex task. We outline key challenges and future research directions to advance XIR in this domain.}, language = {en} }