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

Data mining and knowledge discovery, 2024-09, Vol.38 (5), p.2770-2824

Guidotti, Riccardo

2024

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Benchmarking and survey of explanation methods for black box models

Data mining and knowledge discovery, 2023-09, Vol.37 (5), p.1719-1778

2023

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Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach

Data mining and knowledge discovery, 2024-09, Vol.38 (5), p.2903-2941

2024

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Grouped feature importance and combined features effect plot

Data mining and knowledge discovery, 2022-07, Vol.36 (4), p.1401-1450

2022

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Marginal effects for non-linear prediction functions

Data mining and knowledge discovery, 2024-09, Vol.38 (5), p.2997-3042

2024

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Conclusive local interpretation rules for random forests

Data mining and knowledge discovery, 2022-07, Vol.36 (4), p.1521-1574

2022

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Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection

Proceedings of the First Workshop on Machine Learning for Computing Systems, 2018, p.1-8

2018

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Interpretable machine learning: Fundamental principles and 10 grand challenges

Statistics surveys, 2022-01, Vol.16 (none)

2022

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Opening the Black Box: Interpretable Machine Learning for Geneticists

Trends in genetics, 2020-06, Vol.36 (6), p.442-455

2020

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

Information fusion, 2023-11, Vol.99, p.101805, Article 101805

2023

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Attention-based interpretable neural network for building cooling load prediction

Applied energy, 2021-10, Vol.299, p.117238, Article 117238

2021

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The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Computer graphics forum, 2020-06, Vol.39 (3), p.713-756

2020

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Assessing the tumor immune landscape across multiple spatial scales to differentiate immunotherapy response in metastatic non-small cell lung cancer

Laboratory investigation, 2024-10, p.102148

2024

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Interpretable Machine Learning Model For Heart Disease Prediction

Procedia computer science, 2023, Vol.227, p.439-445

2023

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Explaining the black-box model: A survey of local interpretation methods for deep neural networks

Neurocomputing (Amsterdam), 2021-01, Vol.419, p.168-182

2021

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Effects of heavy metal exposure on hypertension: A machine learning modeling approach

Chemosphere (Oxford), 2023-10, Vol.337, p.139435-139435, Article 139435

2023

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Evaluating the Visualization of What a Deep Neural Network Has Learned

IEEE transaction on neural networks and learning systems, 2017-11, Vol.28 (11), p.2660-2673

2017

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Factual and Counterfactual Explanations for Black Box Decision Making

IEEE intelligent systems, 2019-11, Vol.34 (6), p.14-23

2019

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EnergyStar++: Towards more accurate and explanatory building energy benchmarking

Applied energy, 2020-10, Vol.276, p.115413, Article 115413

2020

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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

IEEE access, 2018-01, Vol.6, p.52138-52160

2018

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Explaining contributions of features towards unfairness in classifiers: A novel threshold-dependent Shapley value-based approach

Engineering applications of artificial intelligence, 2024-12, Vol.138, p.109427, Article 109427

2024

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

ACM computing surveys, 2023-07, Vol.55 (13s), p.1-42, Article 295

2023

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GAN Inversion: A Survey

IEEE transactions on pattern analysis and machine intelligence, 2023-03, Vol.45 (3), p.3121-3138

2023

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A historical perspective of explainable Artificial Intelligence

Wiley interdisciplinary reviews. Data mining and knowledge discovery, 2021-01, Vol.11 (1), p.e1391-n/a

2021

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Explainable, trustworthy, and ethical machine learning for healthcare: A survey

Computers in biology and medicine, 2022-10, Vol.149, p.106043-106043, Article 106043

2022

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

Benchmarking and survey of explanation methods for black box models

Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach

Grouped feature importance and combined features effect plot

Marginal effects for non-linear prediction functions

Conclusive local interpretation rules for random forests

Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection

Interpretable machine learning: Fundamental principles and 10 grand challenges

Opening the Black Box: Interpretable Machine Learning for Geneticists

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

Attention-based interpretable neural network for building cooling load prediction

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Assessing the tumor immune landscape across multiple spatial scales to differentiate immunotherapy response in metastatic non-small cell lung cancer

Interpretable Machine Learning Model For Heart Disease Prediction

Explaining the black-box model: A survey of local interpretation methods for deep neural networks

Effects of heavy metal exposure on hypertension: A machine learning modeling approach

Evaluating the Visualization of What a Deep Neural Network Has Learned

Factual and Counterfactual Explanations for Black Box Decision Making

EnergyStar++: Towards more accurate and explanatory building energy benchmarking

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

Explaining contributions of features towards unfairness in classifiers: A novel threshold-dependent Shapley value-based approach

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

GAN Inversion: A Survey

A historical perspective of explainable Artificial Intelligence

Explainable, trustworthy, and ethical machine learning for healthcare: A survey

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