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 …
Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey
W Ding, M Abdel-Basset, H Hawash, AM Ali - Information Sciences, 2022 - Elsevier
The continuous advancement of Artificial Intelligence (AI) has been revolutionizing the
strategy of decision-making in different life domains. Regardless of this achievement, AI …
strategy of decision-making in different life domains. Regardless of this achievement, AI …
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 …
Explainable AI for time series classification: a review, taxonomy and research directions
Time series data is increasingly used in a wide range of fields, and it is often relied on in
crucial applications and high-stakes decision-making. For instance, sensors generate time …
crucial applications and high-stakes decision-making. For instance, sensors generate time …
Instance-based counterfactual explanations for time series classification
In recent years, there has been a rapidly expanding focus on explaining the predictions
made by black-box AI systems that handle image and tabular data. However, considerably …
made by black-box AI systems that handle image and tabular data. However, considerably …
Explainable AI for medical data: current methods, limitations, and future directions
With the power of parallel processing, large datasets, and fast computational resources,
deep neural networks (DNNs) have outperformed highly trained and experienced human …
deep neural networks (DNNs) have outperformed highly trained and experienced human …
Encoding time-series explanations through self-supervised model behavior consistency
Interpreting time series models is uniquely challenging because it requires identifying both
the location of time series signals that drive model predictions and their matching to an …
the location of time series signals that drive model predictions and their matching to an …
Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging
Explainable artificial intelligence (XAI) has experienced a vast increase in recognition over
the last few years. While the technical developments are manifold, less focus has been …
the last few years. While the technical developments are manifold, less focus has been …
[HTML][HTML] Explainable ai for time series via virtual inspection layers
The field of eXplainable Artificial Intelligence (XAI) has witnessed significant advancements
in recent years. However, the majority of progress has been concentrated in the domains of …
in recent years. However, the majority of progress has been concentrated in the domains of …
Fairness issues, current approaches, and challenges in machine learning models
With the increasing influence of machine learning algorithms in decision-making processes,
concerns about fairness have gained significant attention. This area now offers significant …
concerns about fairness have gained significant attention. This area now offers significant …