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Bias mitigation for machine learning classifiers: A comprehensive survey
This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …
A survey on datasets for fairness‐aware machine learning
As decision‐making increasingly relies on machine learning (ML) and (big) data, the issue
of fairness in data‐driven artificial intelligence systems is receiving increasing attention from …
of fairness in data‐driven artificial intelligence systems is receiving increasing attention from …
Delivering trustworthy AI through formal XAI
The deployment of systems of artificial intelligence (AI) in high-risk settings warrants the
need for trustworthy AI. This crucial requirement is highlighted by recent EU guidelines and …
need for trustworthy AI. This crucial requirement is highlighted by recent EU guidelines and …
On tackling explanation redundancy in decision trees
Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models.
The interpretability of decision trees motivates explainability approaches by so-called …
The interpretability of decision trees motivates explainability approaches by so-called …
Logic-based explainability in machine learning
The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …
On the failings of Shapley values for explainability
Abstract Explainable Artificial Intelligence (XAI) is widely considered to be critical for building
trust into the deployment of systems that integrate the use of machine learning (ML) models …
trust into the deployment of systems that integrate the use of machine learning (ML) models …
The inadequacy of shapley values for explainability
This paper develops a rigorous argument for why the use of Shapley values in explainable
AI (XAI) will necessarily yield provably misleading information about the relative importance …
AI (XAI) will necessarily yield provably misleading information about the relative importance …
Trustworthy artificial intelligence in Alzheimer's disease: state of the art, opportunities, and challenges
Abstract Medical applications of Artificial Intelligence (AI) have consistently shown
remarkable performance in providing medical professionals and patients with support for …
remarkable performance in providing medical professionals and patients with support for …
Predicting and understanding student learning performance using multi-source sparse attention convolutional neural networks
Predicting and understanding student learning performance has been a long-standing task
in learning science, which can benefit personalized teaching and learning. This study shows …
in learning science, which can benefit personalized teaching and learning. This study shows …
A critical survey on fairness benefits of explainable AI
In this critical survey, we analyze typical claims on the relationship between explainable AI
(XAI) and fairness to disentangle the multidimensional relationship between these two …
(XAI) and fairness to disentangle the multidimensional relationship between these two …