A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences

When Artificial Intelligence Models Surpass Physician Performance: Medical Malpractice Liability in an Era of Advanced Artificial Intelligence

(2022) John D. Banja et al.   Journal of the American College of Radiology

Neuro-symbolic artificial intelligence

(2021) Md Kamruzzaman Sarker et al.   AI COMMUNICATIONS

Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists

(2021) Jose P. Amorim et al.   IEEE Reviews in Biomedical Engineering

Preparing Medical Imaging Data for Machine Learning

(2020) Martin J. Willemink et al.   RADIOLOGY

Rule extraction from neural network trained using deep belief network and back propagation

(2020) Manomita Chakraborty et al.   KNOWLEDGE AND INFORMATION SYSTEMS

Concept attribution: Explaining CNN decisions to physicians

(2020) Graziani M. et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians

(2020) Onur Asan et al.   JOURNAL OF MEDICAL INTERNET RESEARCH

“Explaining” machine learning reveals policy challenges

(2020) Diane Coyle et al.   SCIENCE

Demand-Driven Transparency for Monitoring Intelligent Agents

(2020) Mor Vered et al.   IEEE Transactions on Human-Machine Systems

Continuous Learning AI in Radiology: Implementation Principles and Early Applications

(2020) Oleg S. Pianykh et al.   RADIOLOGY

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

(2020) Erico Tjoa et al.   IEEE Transactions on Neural Networks and Learning Systems

Explainability in human–agent systems

(2019) Avi Rosenfeld et al.   AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS

A Misdirected Principle with a Catch: Explicability for AI

(2019) Scott Robbins   MINDS AND MACHINES

Definitions, methods, and applications in interpretable machine learning

(2019) W. James Murdoch et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

(2019) Alejandro Barredo Arrieta et al.   Information Fusion

Methods for interpreting and understanding deep neural networks

(2018) Grégoire Montavon et al.   DIGITAL SIGNAL PROCESSING

A Survey of Methods for Explaining Black Box Models

(2018) Riccardo Guidotti et al.   ACM COMPUTING SURVEYS

A Deep Look into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change

(2018) Olivier Morin et al.   INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS

Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI)

(2018) Amina Adadi et al.   IEEE Access

AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations

(2018) Luciano Floridi et al.   MINDS AND MACHINES

Explanation in artificial intelligence: Insights from the social sciences

(2018) Tim Miller   ARTIFICIAL INTELLIGENCE

Explaining nonlinear classification decisions with deep Taylor decomposition

(2017) Grégoire Montavon et al.   PATTERN RECOGNITION