Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications

Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics

(2021) Jianlong Zhou et al.   Electronics

LINDA-BN: An interpretable probabilistic approach for demystifying black-box predictive models

(2021) Catarina Moreira et al.   DECISION SUPPORT SYSTEMS

Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI

(2021) Andreas Holzinger et al.   Information Fusion

Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information

(2020) Buomsoo Kim et al.   DECISION SUPPORT SYSTEMS

Multimodal medical image fusion algorithm in the era of big data

(2020) Wei Tan et al.   NEURAL COMPUTING & APPLICATIONS

Improving the accuracy of medical diagnosis with causal machine learning

(2020) Jonathan G. Richens et al.   Nature Communications

The Effects of Explainability and Causability on Perception, Trust, and Acceptance: Implications for Explainable AI

(2020) Full Professor Donghee Shin   INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES

Evaluating XAI: A comparison of rule-based and example-based explanations

(2020) Jasper van der Waa et al.   ARTIFICIAL INTELLIGENCE

Unmasking Clever Hans predictors and assessing what machines really learn

(2019) Sebastian Lapuschkin et al.   Nature Communications

Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to?

(2019) Alberto Fernandez et al.   IEEE Computational Intelligence Magazine

The seven tools of causal inference, with reflections on machine learning

(2019) Judea Pearl   COMMUNICATIONS OF THE ACM

The Pragmatic Turn in Explainable Artificial Intelligence (XAI)

(2019) Andrés Páez   MINDS AND MACHINES

Causability and explainabilty of artificial intelligence in medicine

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

Machine Learning Interpretability: A Survey on Methods and Metrics

(2019) Diogo V. Carvalho et al.   Electronics

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

Dissecting racial bias in an algorithm used to manage the health of populations

(2019) Ziad Obermeyer et al.   SCIENCE

The ergodicity problem in economics

(2019) Ole Peters   Nature Physics

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

(2019) Alejandro Barredo Arrieta et al.   Information Fusion

Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews

(2018) Michael Siering et al.   DECISION SUPPORT SYSTEMS

Word embeddings quantify 100 years of gender and ethnic stereotypes

(2018) Nikhil Garg et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

The mythos of model interpretability

(2018) Zachary C. Lipton   COMMUNICATIONS OF THE ACM

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

(2018) Amina Adadi et al.   IEEE Access

Interactive machine learning: experimental evidence for the human in the algorithmic loop

(2018) Andreas Holzinger et al.   APPLIED INTELLIGENCE

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

(2018) John R. Zech et al.   PLOS MEDICINE

Explanation in artificial intelligence: Insights from the social sciences

(2018) Tim Miller   ARTIFICIAL INTELLIGENCE

Semantics derived automatically from language corpora contain human-like biases

(2017) Aylin Caliskan et al.   SCIENCE

Explaining Data-Driven Document Classifications

(2017) David Martens et al.   MIS QUARTERLY

Building machines that learn and think like people

(2016) Brenden M. Lake et al.   BEHAVIORAL AND BRAIN SCIENCES

Cognitive biases associated with medical decisions: a systematic review

(2016) Gustavo Saposnik et al.   BMC Medical Informatics and Decision Making

Computational rationality: A converging paradigm for intelligence in brains, minds, and machines

(2015) S. J. Gershman et al.   SCIENCE

Explaining prediction models and individual predictions with feature contributions

(2013) Erik Štrumbelj et al.   KNOWLEDGE AND INFORMATION SYSTEMS