Explainable artificial intelligence: a comprehensive review
Thanks to the exponential growth in computing power and vast amounts of data, artificial
intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be …
intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be …
[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 …
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 …
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 …
Interpretable machine learning with an ensemble of gradient boosting machines
AV Konstantinov, LV Utkin - Knowledge-Based Systems, 2021 - Elsevier
A method for the local and global interpretation of a black-box model on the basis of the well-
known generalized additive models is proposed. It can be viewed as an extension or a …
known generalized additive models is proposed. It can be viewed as an extension or a …
[PDF][PDF] To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods
The main objective of eXplainable Artificial Intelligence (XAI) is to provide effective
explanations for black-box classifiers. The existing literature lists many desirable properties …
explanations for black-box classifiers. The existing literature lists many desirable properties …
XEM: An explainable-by-design ensemble method for multivariate time series classification
We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series
classification. XEM relies on a new hybrid ensemble method that combines an explicit …
classification. XEM relies on a new hybrid ensemble method that combines an explicit …
[HTML][HTML] Interpretable machine learning techniques in ECG-based heart disease classification: a systematic review
Heart disease is one of the leading causes of mortality throughout the world. Among the
different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non …
different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non …
[HTML][HTML] Mathematical optimization modelling for group counterfactual explanations
Counterfactual Analysis has shown to be a powerful tool in the burgeoning field of
Explainable Artificial Intelligence. In Supervised Classification, this means associating with …
Explainable Artificial Intelligence. In Supervised Classification, this means associating with …
[HTML][HTML] Trusting deep learning natural-language models via local and global explanations
Despite the high accuracy offered by state-of-the-art deep natural-language models (eg,
LSTM, BERT), their application in real-life settings is still widely limited, as they behave like a …
LSTM, BERT), their application in real-life settings is still widely limited, as they behave like a …