A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Markus Langer, Daniel Oster, Timo Speith, et al.
This paper reviews stakeholder desiderata for XAI and proposes a conceptual model to guide interdisciplinary XAI research.
Daniel J. Mathew, Deborah Ebem, Anayo Chukwu Ikegwu, et al.
This paper reviews recent XAI techniques to enhance interpretability and human understanding of complex AI models, addressing transparency challenges.
Johannes Schneider
A survey and conceptualization of explainability for generative AI, introducing new criteria and a research agenda.
Rudresh Dwivedi, Devam Dave, Het Naik, et al.
A comprehensive survey of XAI techniques, taxonomies, and programming frameworks to guide stakeholders in selecting appropriate explainability methods.
David Gunning, David W. Aha
DARPA's XAI program aims to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users.
Erico Tjoa, Cuntai Guan
A survey categorizing interpretability methods in explainable AI, with a focus on medical applications to improve transparency and trust.
Luca Longo, Mario Brčić, Federico Cabitza, et al.
This paper presents a manifesto of 28 open challenges in Explainable AI, categorized into nine areas, to guide future interdisciplinary research.
Sajid Ali, Tamer Abuhmed, Shaker El–Sappagh, et al.
A comprehensive survey of XAI techniques, evaluation methods, tools, datasets, and legal/ethical concerns for trustworthy AI.
Vikas Hassija, Vinay Chamola, Atmesh Mahapatra, et al.
A comprehensive review of explainable AI (XAI) methods for interpreting black-box models, covering frameworks, efficacy, and emerging issues.