A historical perspective of explainable Artificial Intelligence

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

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

Causability and explainabilty of artificial intelligence in medicine

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

Personalised novel and explainable matrix factorisation

(2019) Ludovik Coba et al.   DATA & KNOWLEDGE ENGINEERING

Reconciling deep learning with symbolic artificial intelligence: representing objects and relations

(2019) Marta Garnelo et al.   Current Opinion in Behavioral Sciences

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

(2019) Alejandro Barredo Arrieta et al.   Information Fusion

Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP

(2018) Stephen H. Muggleton et al.   MACHINE LEARNING

A Survey of Methods for Explaining Black Box Models

(2018) Riccardo Guidotti et al.   ACM COMPUTING SURVEYS

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

(2018) Andreas Holzinger et al.   APPLIED INTELLIGENCE

Explanation in artificial intelligence: Insights from the social sciences

(2018) Tim Miller   ARTIFICIAL INTELLIGENCE

Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples

(2017) Tarek R. Besold et al.   MINDS AND MACHINES

A systematic review and taxonomy of explanations in decision support and recommender systems

(2017) Ingrid Nunes et al.   USER MODELING AND USER-ADAPTED INTERACTION

A Taxonomy for Generating Explanations in Recommender Systems

(2017) Gerhard Friedrich et al.   AI MAGAZINE

Upward refinement operators for conceptual blending in the description logic 𝓔 𝓛 + + $\mathcal {E}\mathcal {L}^{++}$

(2016) Roberto Confalonieri et al.   ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE

Recommendation quality, transparency, and website quality for trust-building in recommendation agents

(2016) Mehrbakhsh Nilashi et al.   Electronic Commerce Research and Applications

What makes classification trees comprehensible?

(2016) Rok Piltaver et al.   EXPERT SYSTEMS WITH APPLICATIONS

Explanatory Preferences Shape Learning and Inference

(2016) Tania Lombrozo   TRENDS IN COGNITIVE SCIENCES

Fast relational learning using bottom clause propositionalization with artificial neural networks

(2013) Manoel V. M. França et al.   MACHINE LEARNING

A generalized taxonomy of explanations styles for traditional and social recommender systems

(2011) Alexis Papadimitriou et al.   DATA MINING AND KNOWLEDGE DISCOVERY

An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models

(2010) Johan Huysmans et al.   DECISION SUPPORT SYSTEMS

Matrix Factorization Techniques for Recommender Systems

(2009) Yehuda Koren et al.   COMPUTER

Concept learning in description logics using refinement operators

(2009) Jens Lehmann et al.   MACHINE LEARNING