Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

Deep Learning for Predictive Analytics in Reversible Steganography

(2023) Ching-Chun Chang et al.   IEEE Access

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

(2023) Meike Nauta et al.   ACM COMPUTING SURVEYS

Gender and sex bias in COVID-19 epidemiological data through the lens of causality

(2023) Natalia Díaz-Rodríguez et al.   INFORMATION PROCESSING & MANAGEMENT

Interpretable machine learning: Fundamental principles and 10 grand challenges

(2022) Cynthia Rudin et al.   Statistics Surveys

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

(2022) Bas H.M. van der Velden et al.   MEDICAL IMAGE ANALYSIS

Explaining Aha! moments in artificial agents through IKE-XAI: Implicit Knowledge Extraction for eXplainable AI

(2022) Ikram Chraibi Kaadoud et al.   NEURAL NETWORKS

Learning to select goals in Automated Planning with Deep-Q Learning

(2022) Carlos Núñez-Molina et al.   EXPERT SYSTEMS WITH APPLICATIONS

PLENARY: Explaining black-box models in natural language through fuzzy linguistic summaries

(2022) Katarzyna Kaczmarek-Majer et al.   INFORMATION SCIENCES

Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond

(2022) Xuhong Li et al.   KNOWLEDGE AND INFORMATION SYSTEMS

SkiNet: A deep learning framework for skin lesion diagnosis with uncertainty estimation and explainability

(2022) Rajeev Kumar Singh et al.   PLoS One

Logic Explained Networks

(2022) Gabriele Ciravegna et al.   ARTIFICIAL INTELLIGENCE

A survey on XAI and natural language explanations

(2022) Erik Cambria et al.   INFORMATION PROCESSING & MANAGEMENT

An empirical study on how humans appreciate automated counterfactual explanations which embrace imprecise information

(2022) Ilia Stepin et al.   INFORMATION SCIENCES

Towards a more efficient computation of individual attribute and policy contribution for post-hoc explanation of cooperative multi-agent systems using Myerson values

(2022) Giorgio Angelotti et al.   KNOWLEDGE-BASED SYSTEMS

Greybox XAI: A Neural-Symbolic learning framework to produce interpretable predictions for image classification

(2022) Adrien Bennetot et al.   KNOWLEDGE-BASED SYSTEMS

A Survey on the Explainability of Supervised Machine Learning

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

Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities

(2021) Adrien Bécue et al.   ARTIFICIAL INTELLIGENCE REVIEW

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

(2021) Shaker El-Sappagh et al.   Scientific Reports

Ethical Machines: The Human-centric Use of Artificial Intelligence

(2021) B. Lepri et al.   iScience

A Survey on Bias and Fairness in Machine Learning

(2021) Ninareh Mehrabi et al.   ACM COMPUTING SURVEYS

What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research

(2021) Markus Langer et al.   ARTIFICIAL INTELLIGENCE

Using ontologies to enhance human understandability of global post-hoc explanations of black-box models

(2021) Roberto Confalonieri et al.   ARTIFICIAL INTELLIGENCE

An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference

(2021) M.Z. Naser   AUTOMATION IN CONSTRUCTION

Context-based image explanations for deep neural networks

(2021) Sule Anjomshoae et al.   IMAGE AND VISION COMPUTING

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

(2021) Andreas Holzinger et al.   Information Fusion

Pruning by explaining: A novel criterion for deep neural network pruning

(2021) Seul-Ki Yeom et al.   PATTERN RECOGNITION

Explainable artificial intelligence: an analytical review

(2021) Plamen P. Angelov et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Parallel coordinate order for high‐dimensional data

(2021) Shaima Tilouche et al.   Statistical Analysis and Data Mining

Knowledge graphs as tools for explainable machine learning: A survey

(2021) Ilaria Tiddi et al.   ARTIFICIAL INTELLIGENCE

Medical artificial intelligence

(2021) Karl Stöger et al.   COMMUNICATIONS OF THE ACM

Datasheets for datasets

(2021) Timnit Gebru et al.   COMMUNICATIONS OF THE ACM

Notions of explainability and evaluation approaches for explainable artificial intelligence

(2021) Giulia Vilone et al.   Information Fusion

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

(2021) Guang Yang et al.   Information Fusion

Perturbation-based methods for explaining deep neural networks: A survey

(2021) Maksims Ivanovs et al.   PATTERN RECOGNITION LETTERS

Knowledge graph-based rich and confidentiality preserving Explainable Artificial Intelligence (XAI)

(2021) Jože M. Rožanec et al.   Information Fusion

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

(2021) Yu-Liang Chou et al.   Information Fusion

EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: The MonuMAI cultural heritage use case

(2021) Natalia Díaz-Rodríguez et al.   Information Fusion

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

Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence

(2020) Soheyl Khalilpourazari et al.   JOURNAL OF INTELLIGENT MANUFACTURING

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

Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities

(2020) Christian Meske et al.   INFORMATION SYSTEMS MANAGEMENT

If deep learning is the answer, what is the question?

(2020) Andrew Saxe et al.   NATURE REVIEWS NEUROSCIENCE

Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms

(2020) Thilo Rieg et al.   PLoS One

A survey on deep learning in medicine: Why, how and when?

(2020) Francesco Piccialli et al.   Information Fusion

Explainability in deep reinforcement learning

(2020) Alexandre Heuillet et al.   KNOWLEDGE-BASED SYSTEMS

Unmasking Clever Hans predictors and assessing what machines really learn

(2019) Sebastian Lapuschkin et al.   Nature Communications

Deep learning and process understanding for data-driven Earth system science

(2019) Markus Reichstein et al.   NATURE

A game-based approximate verification of deep neural networks with provable guarantees

(2019) Min Wu et al.   THEORETICAL COMPUTER SCIENCE

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

Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability

(2019) Mark Coeckelbergh   SCIENCE AND ENGINEERING ETHICS

Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations

(2019) Fred Hohman et al.   IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

XAI—Explainable artificial intelligence

(2019) David Gunning et al.   Science Robotics

Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges

(2019) Timothée Lesort et al.   Information Fusion

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

Analyzing the Training Processes of Deep Generative Models

(2018) Mengchen Liu et al.   IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models

(2018) Minsuk Kahng et al.   IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks

(2018) Hendrik Strobelt et al.   IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks

(2018) Nicola Pezzotti et al.   IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

Visual interpretability for deep learning: a survey

(2018) Quan-shi Zhang et al.   Frontiers of Information Technology & Electronic Engineering

A Survey of Methods for Explaining Black Box Models

(2018) Riccardo Guidotti et al.   ACM COMPUTING SURVEYS

The mythos of model interpretability

(2018) Zachary C. Lipton   COMMUNICATIONS OF THE ACM

State representation learning for control: An overview

(2018) Timothée Lesort et al.   NEURAL NETWORKS

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

Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies

(2017) Eunsuk Chong et al.   EXPERT SYSTEMS WITH APPLICATIONS

Regulating Autonomous Systems: Beyond Standards

(2017) David Danks et al.   IEEE INTELLIGENT SYSTEMS

Towards Better Analysis of Deep Convolutional Neural Networks

(2017) Mengchen Liu et al.   IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

Explaining classifier decisions linguistically for stimulating and improving operators labeling behavior

(2017) Edwin Lughofer et al.   INFORMATION SCIENCES

Distribution-Free Predictive Inference for Regression

(2017) Jing Lei et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

Auditing black-box models for indirect influence

(2017) Philip Adler et al.   KNOWLEDGE AND INFORMATION SYSTEMS

Explaining nonlinear classification decisions with deep Taylor decomposition

(2017) Grégoire Montavon et al.   PATTERN RECOGNITION

Evaluating the Visualization of What a Deep Neural Network Has Learned

(2017) Wojciech Samek et al.   IEEE Transactions on Neural Networks and Learning Systems

Power to the People: The Role of Humans in Interactive Machine Learning

(2017) Saleema Amershi et al.   AI MAGAZINE

An Uncertainty-Aware Approach for Exploratory Microblog Retrieval

(2016) Mengchen Liu et al.   IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

TopicPanorama: A Full Picture of Relevant Topics

(2016) Xiting Wang et al.   IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

Disease Inference from Health-Related Questions via Sparse Deep Learning

(2015) Liqiang Nie et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

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

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

(2015) Sebastian Bach et al.   PLoS One

Ontology of core data mining entities

(2014) Panče Panov et al.   DATA MINING AND KNOWLEDGE DISCOVERY

A peek into the black box: exploring classifiers by randomization

(2014) Andreas Henelius et al.   DATA MINING AND KNOWLEDGE DISCOVERY

You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems

(2014) Alex Groce et al.   IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data

(2014) Josua Krause et al.   IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

How should I explain? A comparison of different explanation types for recommender systems

(2014) Fatih Gedikli et al.   INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES

RULES-IT: incremental transfer learning with RULES family

(2014) Hebah Elgibreen et al.   Frontiers of Computer Science

The Construct of State-Level Suspicion

(2013) Philip Bobko et al.   HUMAN FACTORS

I Trust It, but I Don’t Know Why

(2012) Stephanie M. Merritt et al.   HUMAN FACTORS

Using sensitivity analysis and visualization techniques to open black box data mining models

(2012) Paulo Cortez et al.   INFORMATION SCIENCES

Prototype selection for interpretable classification

(2011) Jacob Bien et al.   Annals of Applied Statistics

Performance of classification models from a user perspective

(2011) David Martens et al.   DECISION SUPPORT SYSTEMS

EDISC: A Class-Tailored Discretization Technique for Rule-Based Classification

(2011) Khurram Shehzad   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems

(2011) M. Gethsiyal Augasta et al.   NEURAL PROCESSING LETTERS

Building comprehensible customer churn prediction models with advanced rule induction techniques

(2010) Wouter Verbeke et al.   EXPERT SYSTEMS WITH APPLICATIONS

A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner

(2009) Lale Özbakır et al.   APPLIED SOFT COMPUTING

Does projection into use improve trust and exploration? An example with a cruise control system

(2009) Béatrice Cahour et al.   SAFETY SCIENCE

A simple and fast algorithm for K-medoids clustering

(2008) Hae-Sang Park et al.   EXPERT SYSTEMS WITH APPLICATIONS

Daytime Arctic Cloud Detection Based on Multi-Angle Satellite Data With Case Studies

(2008) Tao Shi et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

The effects of transparency on trust in and acceptance of a content-based art recommender

(2008) Henriette Cramer et al.   USER MODELING AND USER-ADAPTED INTERACTION

Rule extraction from trained adaptive neural networks using artificial immune systems

(2007) Humar Kahramanli et al.   EXPERT SYSTEMS WITH APPLICATIONS