Counterfactual explanations and how to find them: literature review and benchmarking
R Guidotti - Data Mining and Knowledge Discovery, 2024 - Springer
Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …
uninterpretable classifiers. One of the most valuable types of explanation consists of …
A survey on neural network interpretability
Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …
their black-box nature. The interpretability issue affects people's trust on deep learning …
[HTML][HTML] Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …
[HTML][HTML] Explainable AI for operational research: A defining framework, methods, applications, and a research agenda
The ability to understand and explain the outcomes of data analysis methods, with regard to
aiding decision-making, has become a critical requirement for many applications. For …
aiding decision-making, has become a critical requirement for many applications. For …
Trustworthy AI: From principles to practices
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …
of various systems based on it. However, many current AI systems are found vulnerable to …
A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts
G Schwalbe, B Finzel - Data Mining and Knowledge Discovery, 2024 - Springer
In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation
criteria have been developed within the research field of explainable artificial intelligence …
criteria have been developed within the research field of explainable artificial intelligence …
Understanding the impact of explanations on advice-taking: a user study for AI-based clinical Decision Support Systems
The field of eXplainable Artificial Intelligence (XAI) focuses on providing explanations for AI
systems' decisions. XAI applications to AI-based Clinical Decision Support Systems (DSS) …
systems' decisions. XAI applications to AI-based Clinical Decision Support Systems (DSS) …
[HTML][HTML] The explainability paradox: Challenges for xAI in digital pathology
The increasing prevalence of digitised workflows in diagnostic pathology opens the door to
life-saving applications of artificial intelligence (AI). Explainability is identified as a critical …
life-saving applications of artificial intelligence (AI). Explainability is identified as a critical …
Cf-gnnexplainer: Counterfactual explanations for graph neural networks
Given the increasing promise of graph neural networks (GNNs) in real-world applications,
several methods have been developed for explaining their predictions. Existing methods for …
several methods have been developed for explaining their predictions. Existing methods for …
Explainable artificial intelligence (XAI) post-hoc explainability methods: Risks and limitations in non-discrimination law
D Vale, A El-Sharif, M Ali - AI and Ethics, 2022 - Springer
Organizations are increasingly employing complex black-box machine learning models in
high-stakes decision-making. A popular approach to addressing the problem of opacity of …
high-stakes decision-making. A popular approach to addressing the problem of opacity of …