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 …
Fair Scratch Tickets: Finding Fair Sparse Networks Without Weight Training
Recent studies suggest that computer vision models come at the risk of compromising
fairness. There are extensive works to alleviate unfairness in computer vision using pre …
fairness. There are extensive works to alleviate unfairness in computer vision using pre …
[HTML][HTML] Fairness metrics for health AI: we have a long way to go
The use of Artificial Intelligence (AI) is on track to revolutionize healthcare, with performance
in medical tasks such as clinical diagnosis often being comparable to expert-level accuracy …
in medical tasks such as clinical diagnosis often being comparable to expert-level accuracy …
Machine learning in computational histopathology: Challenges and opportunities
Digital histopathological images, high‐resolution images of stained tissue samples, are a
vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state …
vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state …
Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis
Trustworthy deployment of deep learning medical imaging models into real-world clinical
practice requires that they be calibrated. However, models that are well calibrated overall …
practice requires that they be calibrated. However, models that are well calibrated overall …
No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging
As machine learning methods gain prominence within clinical decision-making, addressing
fairness concerns becomes increasingly urgent. Despite considerable work dedicated to …
fairness concerns becomes increasingly urgent. Despite considerable work dedicated to …
Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics
Deep learning-based recognition systems are deployed at scale for several real-world
applications that inevitably involve our social life. Although being of great support when …
applications that inevitably involve our social life. Although being of great support when …
Fairness in AI and Its Long-Term Implications on Society
Successful deployment of artificial intelligence (AI) in various settings has led to numerous
positive outcomes for individuals and society. However, AI systems have also been shown to …
positive outcomes for individuals and society. However, AI systems have also been shown to …
Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis
Although deep learning (DL) models have shown great success in many medical image
analysis tasks, deployment of the resulting models into real clinical contexts requires:(1) that …
analysis tasks, deployment of the resulting models into real clinical contexts requires:(1) that …
The Role of Subgroup Separability in Group-Fair Medical Image Classification
We investigate performance disparities in deep classifiers. We find that the ability of
classifiers to separate individuals into subgroups varies substantially across medical …
classifiers to separate individuals into subgroups varies substantially across medical …