Algorithmic fairness in artificial intelligence for medicine and healthcare
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 …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
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 …
Sample selection for fair and robust training
Fairness and robustness are critical elements of Trustworthy AI that need to be addressed
together. Fairness is about learning an unbiased model while robustness is about learning …
together. Fairness is about learning an unbiased model while robustness is about learning …
[HTML][HTML] The butterfly effect in artificial intelligence systems: Implications for AI bias and fairness
E Ferrara - Machine Learning with Applications, 2024 - Elsevier
The concept of the Butterfly Effect, derived from chaos theory, highlights how seemingly
minor changes can lead to significant, unpredictable outcomes in complex systems. This …
minor changes can lead to significant, unpredictable outcomes in complex systems. This …
Preserving the fairness guarantees of classifiers in changing environments: a survey
The impact of automated decision-making systems on human lives is growing, emphasizing
the need for these systems to be not only accurate but also fair. The field of algorithmic …
the need for these systems to be not only accurate but also fair. The field of algorithmic …
Are my deep learning systems fair? An empirical study of fixed-seed training
Deep learning (DL) systems have been gaining popularity in critical tasks such as credit
evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL …
evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL …
Interpretable data-based explanations for fairness debugging
A wide variety of fairness metrics and eXplainable Artificial Intelligence (XAI) approaches
have been proposed in the literature to identify bias in machine learning models that are …
have been proposed in the literature to identify bias in machine learning models that are …
Fairness violations and mitigation under covariate shift
We study the problem of learning fair prediction models for unseen test sets distributed
differently from the train set. Stability against changes in data distribution is an important …
differently from the train set. Stability against changes in data distribution is an important …
[HTML][HTML] Algorithmic fairness datasets: the story so far
Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …
decisions, directly impacting people's well-being. As a result, a growing community of …
Fairness guarantees under demographic shift
Recent studies found that using machine learning for social applications can lead to
injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes. To …
injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes. To …