Algorithmic fairness in artificial intelligence for medicine and healthcare

RJ Chen, JJ Wang, DFK Williamson, TY Chen… - Nature biomedical …, 2023 - nature.com
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 …

Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies

E Ferrara - Sci, 2023 - mdpi.com
The significant advancements in applying artificial intelligence (AI) to healthcare decision-
making, medical diagnosis, and other domains have simultaneously raised concerns about …

Should chatgpt be biased? challenges and risks of bias in large language models

E Ferrara - arXiv preprint arXiv:2304.03738, 2023 - arxiv.org
As the capabilities of generative language models continue to advance, the implications of
biases ingrained within these models have garnered increasing attention from researchers …

Bias mitigation for machine learning classifiers: A comprehensive survey

M Hort, Z Chen, JM Zhang, M Harman… - ACM Journal on …, 2024 - dl.acm.org
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 …

Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges

N Rodríguez-Barroso, D Jiménez-López, MV Luzón… - Information …, 2023 - Elsevier
Federated learning is a machine learning paradigm that emerges as a solution to the privacy-
preservation demands in artificial intelligence. As machine learning, federated learning is …

Privacy and fairness in Federated learning: on the perspective of Tradeoff

H Chen, T Zhu, T Zhang, W Zhou, PS Yu - ACM Computing Surveys, 2023 - dl.acm.org
Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …

Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning

J So, C He, CS Yang, S Li, Q Yu… - Proceedings of …, 2022 - proceedings.mlsys.org
Secure model aggregation is a key component of federated learning (FL) that aims at
protecting the privacy of each user's individual model while allowing for their global …

Federated learning for generalization, robustness, fairness: A survey and benchmark

W Huang, M Ye, Z Shi, G Wan, H Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Social bot detection in the age of ChatGPT: Challenges and opportunities

E Ferrara - First Monday, 2023 - firstmonday.org
We present a comprehensive overview of the challenges and opportunities in social bot
detection in the context of the rise of sophisticated AI-based chatbots. By examining the state …

[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 …