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 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 …
[PDF][PDF] Counterfactual explanations for machine learning: A review
Abstract Machine learning plays a role in many deployed decision systems, often in ways
that are difficult or impossible to understand by human stakeholders. Explaining, in a human …
that are difficult or impossible to understand by human stakeholders. Explaining, in a human …
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 …
[HTML][HTML] Towards multi-modal causability with graph neural networks enabling information fusion for explainable AI
AI is remarkably successful and outperforms human experts in certain tasks, even in
complex domains such as medicine. Humans on the other hand are experts at multi-modal …
complex domains such as medicine. Humans on the other hand are experts at multi-modal …
Counterfactual explanations and algorithmic recourses for machine learning: A review
Machine learning plays a role in many deployed decision systems, often in ways that are
difficult or impossible to understand by human stakeholders. Explaining, in a human …
difficult or impossible to understand by human stakeholders. Explaining, in a human …
[HTML][HTML] A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Recommender systems have significantly developed in recent years in parallel with the
witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) …
witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) …
The next frontier: AI we can really trust
A Holzinger - Joint European conference on machine learning and …, 2021 - Springer
Enormous advances in the domain of statistical machine learning, the availability of large
amounts of training data, and increasing computing power have made Artificial Intelligence …
amounts of training data, and increasing computing power have made Artificial Intelligence …
A survey on artificial intelligence (ai) and explainable ai in air traffic management: Current trends and development with future research trajectory
Air Traffic Management (ATM) will be more complex in the coming decades due to the
growth and increased complexity of aviation and has to be improved in order to maintain …
growth and increased complexity of aviation and has to be improved in order to maintain …
Mathematical optimization in classification and regression trees
Classification and regression trees, as well as their variants, are off-the-shelf methods in
Machine Learning. In this paper, we review recent contributions within the Continuous …
Machine Learning. In this paper, we review recent contributions within the Continuous …