Counterfactual explanations and how to find them: literature review and benchmarking

Explainable image classification with evidence counterfactual

(2022) Tom Vermeire et al.   PATTERN ANALYSIS AND APPLICATIONS

GLocalX - From Local to Global Explanations of Black Box AI Models

(2021) Mattia Setzu et al.   ARTIFICIAL INTELLIGENCE

Statistical stability indices for LIME: Obtaining reliable explanations for machine learning models

(2021) Giorgio Visani et al.   JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY

A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data

(2021) Raphael Mazzine Barbosa de Oliveira et al.   Applied Sciences-Basel

A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C

(2020) Yanou Ramon et al.   Advances in Data Analysis and Classification

Evaluating local explanation methods on ground truth

(2020) Riccardo Guidotti   ARTIFICIAL INTELLIGENCE

DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models

(2020) Furui Cheng et al.   IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

Machine Learning Interpretability: A Survey on Methods and Metrics

(2019) Diogo V. Carvalho et al.   Electronics

AttGAN: Facial Attribute Editing by Only Changing What You Want

(2019) Zhenliang He et al.   IEEE TRANSACTIONS ON IMAGE PROCESSING

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

Factual and Counterfactual Explanations for Black Box Decision Making

(2019) Riccardo Guidotti et al.   IEEE INTELLIGENT SYSTEMS

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

Explanation in artificial intelligence: Insights from the social sciences

(2018) Tim Miller   ARTIFICIAL INTELLIGENCE

Explaining Data-Driven Document Classifications

(2017) David Martens et al.   MIS QUARTERLY

The power of possibility: causal learning, counterfactual reasoning, and pretend play

(2012) D. Buchsbaum et al.   PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES

Prototype selection for interpretable classification

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

The Inverse Classification Problem

(2010) Charu C. Aggarwal et al.   JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY

A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

(2009) Amir Beck et al.   SIAM Journal on Imaging Sciences

Nearest-Neighbor Methods in Learning and Vision (Shakhnarovich, G. et al., Eds.; 2006) [Book review]

(2008) F. Gianfelici   IEEE TRANSACTIONS ON NEURAL NETWORKS