Expectation: Personalized Explainable Artificial Intelligence for Decentralized Agents with Heterogeneous Knowledge

by Davide Calvaresi, Giovanni Ciatto, Amro Najjar, Reyhan Aydoğan, Leon Van der Torre, Andrea Omicini, and Michael I. Schumacher Abstract Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies to interpret and explain machine learning (ML) predictors. To date, many initiatives have been proposed. Nevertheless, current research efforts mainly focus on methods tailored to specific ML tasks and algorithms, such as image classification and sentiment analysis.
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Integration of local and global features explanation with global rules extraction and generation tools

by Contreras, Victor and Schumacher, Michael and Calvaresi, Davide Abstract TBD How to access TBD How to cite Bibtex @InCollection{csc-extraamas2022-local-global, author = {Contreras, Victor and Schumacher, Michael and Calvaresi, Davide}, booktitle = {Explainable and Transparent AI and Multi-Agent Systems. Fourth International Workshop, EXTRAAMAS 2022, Virtual Event, May, 2022, Revised Selected Papers}, publisher = {Springer Nature}, title = {Integration of local and global features explanation with global rules extraction and generation tools}, year = {2022}, address = {Basel, Switzerland}, editor = {Calvaresi, Davide and Najjar, Amro and Winikoff, Michael and Fr{\"a}mling, Kary}, series = {Lecture Notes in Computer Science}, keywords = {Local explainability; Global explainability; Feature ranking; rule extraction}, subseries = {Lecture Notes in Artificial Intelligence}, }