Interpretable and explainable machine learning: A methods‐centric overview with concrete examples

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI

(2023) Meike Nauta et al.   ACM COMPUTING SURVEYS

Methods and tools for causal discovery and causal inference

(2022) Ana Rita Nogueira et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Interpretable machine learning: Fundamental principles and 10 grand challenges

(2022) Cynthia Rudin et al.   Statistics Surveys

A Survey on the Explainability of Supervised Machine Learning

(2021) Nadia Burkart et al.   JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH

Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis

(2021) Ricards Marcinkevics et al.   Frontiers in Pediatrics

Kandinsky Patterns

(2021) Heimo Müller et al.   ARTIFICIAL INTELLIGENCE

Interpretable machine learning for genomics

(2021) David S. Watson   HUMAN GENETICS

SurvNAM: The machine learning survival model explanation

(2021) Lev V. Utkin et al.   NEURAL NETWORKS

A deep learning system for differential diagnosis of skin diseases

(2020) Yuan Liu et al.   NATURE MEDICINE

AI Feynman: A physics-inspired method for symbolic regression

(2020) Silviu-Marian Udrescu et al.   Science Advances

Opening the Black Box: Interpretable Machine Learning for Geneticists

(2020) Christina B. Azodi et al.   TRENDS IN GENETICS

Interpretability of machine learning‐based prediction models in healthcare

(2020) Gregor Stiglic et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Explainable artificial intelligence and machine learning: A reality rooted perspective

(2020) Frank Emmert‐Streib et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Understanding the role of individual units in a deep neural network

(2020) David Bau et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A historical perspective of explainable Artificial Intelligence

(2020) Roberto Confalonieri et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Explainable AI: A Review of Machine Learning Interpretability Methods

(2020) Pantelis Linardatos et al.   Entropy

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

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

Causability and explainabilty of artificial intelligence in medicine

(2019) Andreas Holzinger et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

DARPA’s Explainable Artificial Intelligence (XAI) Program

(2019) David Gunning et al.   AI MAGAZINE

Scalable Visualization Methods for Modern Generalized Additive Models

(2019) Matteo Fasiolo et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

Machine Learning Interpretability: A Survey on Methods and Metrics

(2019) Diogo V. Carvalho et al.   Electronics

Deep Knockoffs

(2019) Yaniv Romano et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

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

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

(2019) K. T. Schütt et al.   Nature Communications

Techniques for interpretable machine learning

(2019) Mengnan Du et al.   COMMUNICATIONS OF THE ACM

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

(2019) Alejandro Barredo Arrieta et al.   Information Fusion

Sequential regulatory activity prediction across chromosomes with convolutional neural networks

(2018) David R. Kelley et al.   GENOME RESEARCH

Panning for gold: ‘model-X’ knockoffs for high dimensional controlled variable selection

(2018) Emmanuel Candès et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

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

Explanation in artificial intelligence: Insights from the social sciences

(2018) Tim Miller   ARTIFICIAL INTELLIGENCE

Towards deep learning with segregated dendrites

(2017) Jordan Guerguiev et al.   eLife

Controlling the false discovery rate via knockoffs

(2015) Rina Foygel Barber et al.   ANNALS OF STATISTICS

Supersparse linear integer models for optimized medical scoring systems

(2015) Berk Ustun et al.   MACHINE LEARNING

A Sparse-Group Lasso

(2012) Noah Simon et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

Classification and regression trees

(2011) Wei-Yin Loh   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

An Introduction to Causal Inference

(2010) Judea Pearl  

Predictive learning via rule ensembles

(2008) Jerome H. Friedman et al.   Annals of Applied Statistics