tag : interpretability

Counterfactual Explanations for Machine Learning: Challenges Revisited (2021) — 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

On the integration of symbolic and sub-symbolic techniques for XAI: A survey (Intelligenza Artificiale, 2020) — Roberta Calegari, Giovanni Ciatto, Andrea Omicini

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

The mythos of model interpretability (2018) — Zachary C. Lipton