In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

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 …

Data-driven algorithms are only as good as the data they work with, while datasets,
especially social data, often fail to represent minorities adequately. Representation Bias in …

The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning
varies depending on context and community. Researchers either focus on narrow technical …

In the evolving field of Artificial Intelligence, concerns have arisen about the opacity of
certain models and their potential biases. This study aims to improve fairness and …

The exponential growth of big data in RNA biology (RB) has led to the development of deep
learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL …

Despite impressive performance on standard benchmarks, deep neural networks are often
brittle when deployed in real-world systems. Consequently, recent research has focused on …

A trade-off between accuracy and fairness is almost taken as a given in the existing literature
on fairness in machine learning. Yet, it is not preordained that accuracy should decrease …

Recent natural language processing (NLP) techniques have accomplished high
performance on benchmark data sets, primarily due to the significant improvement in the …

Ethics of Artificial Intelligence (AI) is a growing research field that has emerged in response
to the challenges related to AI. Transparency poses a key challenge for implementing AI …