Explainable AI: A Review of Machine Learning Interpretability Methods

Visualizing the effects of predictor variables in black box supervised learning models

(2020) Daniel W. Apley et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

DARPA’s Explainable Artificial Intelligence (XAI) Program

(2019) David Gunning et al.   AI MAGAZINE

One Pixel Attack for Fooling Deep Neural Networks

(2019) Jiawei Su et al.   IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

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

A Survey of Methods for Explaining Black Box Models

(2018) Riccardo Guidotti et al.   ACM COMPUTING SURVEYS

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

(2018) Amina Adadi et al.   IEEE Access

A guide to deep learning in healthcare

(2018) Andre Esteva et al.   NATURE MEDICINE

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

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

(2015) Alex Goldstein et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

Supersparse linear integer models for optimized medical scoring systems

(2015) Berk Ustun et al.   MACHINE LEARNING

Deep learning

(2015) Yann LeCun et al.   NATURE

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

(2015) Sebastian Bach et al.   PLoS One

Global sensitivity measures from given data

(2012) Elmar Plischke et al.   EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Bias correction for the estimation of sensitivity indices based on random balance designs

(2012) Jean-Yves Tissot et al.   RELIABILITY ENGINEERING & SYSTEM SAFETY

Data preprocessing techniques for classification without discrimination

(2011) Faisal Kamiran et al.   KNOWLEDGE AND INFORMATION SYSTEMS

Permutation importance: a corrected feature importance measure

(2010) André Altmann et al.   BIOINFORMATICS

Three naive Bayes approaches for discrimination-free classification

(2010) Toon Calders et al.   DATA MINING AND KNOWLEDGE DISCOVERY

Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index

(2009) Andrea Saltelli et al.   COMPUTER PHYSICS COMMUNICATIONS

Derivative based global sensitivity measures and their link with global sensitivity indices

(2009) I.M. Sobol’ et al.   MATHEMATICS AND COMPUTERS IN SIMULATION

An effective algorithm for computing global sensitivity indices (EASI)

(2009) Elmar Plischke   RELIABILITY ENGINEERING & SYSTEM SAFETY