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 …
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 …
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 …
biases ingrained within these models have garnered increasing attention from researchers …
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 …
Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges
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 …
preservation demands in artificial intelligence. As machine learning, federated learning is …
Privacy and fairness in Federated learning: on the perspective of Tradeoff
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 …
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
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 …
protecting the privacy of each user's individual model while allowing for their global …
Federated learning for generalization, robustness, fairness: A survey and benchmark
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …
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 …
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 …
minor changes can lead to significant, unpredictable outcomes in complex systems. This …