By developing a fairer AI for applications in ophthalmology, we can mitigate these biases and provide fair and unbiased eye care4,5. Despite the remarkable diagnostic accuracy of data-driven deep neural networks (DNNs), DNNs are susceptible to bias due to the prioritization of minimizing overall ...
Almost 30 years ago, an Institute of Medicine report1documented racial disparities in health care and suggested that many disparities might be due to clinician bias (or prejudice) against patients from racially and ethnically minoritized groups and stereotypes about the behavior or health of patients ...
Implicit bias (also referred to as unconscious bias) is a term that describes when an individual attributes particular characteristics, traits, or stereotypes to members of a particular group without their conscious knowledge (Banaji & Greenwald,1995). These biases function without conscious awareness o...
The adoption and maintenance of physical activity (PA) is an important health behavior. This paper presents the first comprehensive empirical test of the Physical Activity Adoption and Maintenance (PAAM) model, which proposes that a combination of explicit (e.g., intention) and implicit (e.g., ...
Recent studies have explored strategies to address bias in CRS datasets. Fairness and system reliability can be considerably improved by addressing selection and popularity bias via counterfactual data simulation and reinforcement learning [25]. Studies have also investigated parameter-efficient conversational...
Health2nd Edition of Treatment of Foot and Ankle Injury and Public Health2nd Edition of Trends in Sustainable Buildings and Infrastructure2nd Edition of Water Sports Implications for Training, Environment and Health2nd Edition: Advances in Maternal and Child Healthcare2nd Edition: Advances in ...
Previous literature on deep learning theory has focused on implicit bias with small learning rates. In this work, we explore the impact of data separability on the implicit bias of deep learning algorithms under the large learning rate. Using deep linear networks for binary classification with the...