Explainable Artificial Intelligence (XAI)

TopicLeading institutions, researchers & key papers

This cluster of papers focuses on Explainable Artificial Intelligence (XAI) and the development of interpretable models, visual explanations, and responsible machine learning interpretability. It explores concepts, challenges, and opportunities in XAI, including the use of gradient-based localization, understanding deep neural networks, feature importance, and addressing black box models. The papers also discuss the responsibility and ethical considerations in AI.

136

Works

IDs:OpenAlex

How has Explainable Artificial Intelligence (XAI)'s publication output changed over time?

ScholarIQpublication output · 2019–2024

Output declined50% over the shown period — from 2 works in 2019 to 1 in 2024.

201920202021202220232024

What are the most-cited papers on Explainable Artificial Intelligence (XAI)?

ScholarIQmost cited works

From local explanations to global understanding with explainable AI for trees

Scott Lundberg, Gabriel Erion, Hugh Chen, Alex J. DeGrave, Jordan M. Prutkin, Bala G. Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, Su‐In Lee

S2912241403. 20209,466 CitationsOPEN ACCESS

XAI—Explainable artificial intelligence

David Gunning, Mark Stefik, Jaesik Choi, Tim Miller, Simone Stumpf, Guang‐Zhong Yang

S4210213233. 20191,939 CitationsOPEN ACCESS

Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

Vikas Hassija, Vinay Chamola, Atmesh Mahapatra, Abhinandan Singal, Divyansh Goel, Kaizhu Huang, Simone Scardapane, Indro Spinelli, Mufti Mahmud, Amir Hussain

S133078663. 20231,859 CitationsOPEN ACCESS

Explainable artificial intelligence: a comprehensive review

Dang Lien Minh, Hanxiang Wang, Yanfen Li, Tan N. Nguyen

Artificial Intelligence Review. 2021904 Citations

Transparency and trust in artificial intelligence systems

Philipp Schmidt, Felix Bießmann, Timm Teubner

S119153320. 2020413 Citations