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Counterfactual explanations and how to find them: literature review and benchmarking
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 …
[HTML][HTML] A systematic review of explainable artificial intelligence in terms of different application domains and tasks
Artificial intelligence (AI) and machine learning (ML) have recently been radically improved
and are now being employed in almost every application domain to develop automated or …
and are now being employed in almost every application domain to develop automated or …
[HTML][HTML] Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation
Abstract Trustworthy Artificial Intelligence (AI) is based on seven technical requirements
sustained over three main pillars that should be met throughout the system's entire life cycle …
sustained over three main pillars that should be met throughout the system's entire life cycle …
A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence
A number of algorithms in the field of artificial intelligence offer poorly interpretable
decisions. To disclose the reasoning behind such algorithms, their output can be explained …
decisions. To disclose the reasoning behind such algorithms, their output can be explained …
Benchmarking and survey of explanation methods for black box models
The rise of sophisticated black-box machine learning models in Artificial Intelligence
systems has prompted the need for explanation methods that reveal how these models work …
systems has prompted the need for explanation methods that reveal how these models work …
[HTML][HTML] Deterministic local interpretable model-agnostic explanations for stable explainability
Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique used to
increase the interpretability and explainability of black box Machine Learning (ML) …
increase the interpretability and explainability of black box Machine Learning (ML) …
Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare
Abstract Background and Objective: Artificial Intelligence (AI) has shown significant
advancements across several industries, including healthcare, using better fusion …
advancements across several industries, including healthcare, using better fusion …
Bias and discrimination in AI: a cross-disciplinary perspective
Operating at a large scale and impacting large groups of people, automated systems can
make consequential and sometimes contestable decisions. Automated decisions can impact …
make consequential and sometimes contestable decisions. Automated decisions can impact …
[HTML][HTML] Classification of explainable artificial intelligence methods through their output formats
Machine and deep learning have proven their utility to generate data-driven models with
high accuracy and precision. However, their non-linear, complex structures are often difficult …
high accuracy and precision. However, their non-linear, complex structures are often difficult …
Interpretability research of deep learning: A literature survey
Deep learning (DL) has been widely used in various fields. However, its black-box nature
limits people's understanding and trust in its decision-making process. Therefore, it becomes …
limits people's understanding and trust in its decision-making process. Therefore, it becomes …