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 …

Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …

Artificial intelligence (AI) represents technologies with human‐like cognitive abilities to learn,
perform, and make decisions. AI in precision agriculture (PA) enables farmers and farm …

Statistical measures for group fairness in machine learning reflect the gap in performance of
algorithms across different groups. These measures, however, exhibit a high variance …

Alzheimer's disease (AD) is one of the most common neurodegenerative disorders
presenting irreversible progression of cognitive impairment. How to identify AD as early as …

Machine learning (ML) methods are proliferating in scientific research. However, the
adoption of these methods has been accompanied by failures of validity, reproducibility, and …

Recently, there has been an increase in efforts to understand how large language models
(LLMs) propagate and amplify social biases. Several works have utilized templates for …

Variance in predictions across different trained models is a significant, under-explored
source of error in fair binary classification. In practice, the variance on some data examples …

" The power of a generalization system follows directly from its biases"(Mitchell 1980).
Today, CNNs are incredibly powerful generalisation systems--but to what degree have we …

In real-world recommender systems, implicitly collected user feedback, while abundant,
often includes noisy false-positive and false-negative interactions. The possible …