tag : explainability
9 publications
On the Evaluation of the Symbolic Knowledge Extracted from Black Boxes (2024) — Federico Sabbatini, Roberta Calegari Bottom-Up and Top-Down Workflows for Hypercube- and Clustering-based Knowledge Extractors (EXTRAAMAS 2023@AAMAS 2023) — Federico Sabbatini, Roberta Calegari Evaluation Metrics for Symbolic Knowledge Extracted from Machine Learning Black Boxes: A Discussion Paper (2022) — Federico Sabbatini, Roberta Calegari Counterfactual Explanations for Machine Learning: Challenges Revisited (2021) — Sahil Verma, John P. Dickerson, Keegan Hines Arg-tuProlog: A tuProlog-based argumentation framework (CILC 2020) — Giuseppe Pisano, Roberta Calegari, Andrea Omicini, Giovanni Sartor An Abstract Framework for Agent-Based Explanations in AI (AAMAS 2020) — Giovanni Ciatto, Davide Calvaresi, Michael I. Schumacher, Andrea Omicini Causal Interpretability for Machine Learning – Problems, Methods and Evaluation (2020) — Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, Huan Liu Counterfactual Explanations for Machine Learning: A Review (2020) — Sahil Verma, John P. Dickerson, Keegan Hines Agent-Based Explanations in AI: Towards an Abstract Framework (EXTRAAMAS 2020@AAMAS 2020) — Giovanni Ciatto, Michael I. Schumacher, Andrea Omicini, Davide Calvaresi