Abstract
Recent Advancements in artificial intelligence (AI) have raised significant ethical and philosophical challenges, particularly related to unequal access to healthcare.
Progress has been made in understanding the impact of AI on health inequity, such as technology-based studies aiming to define and mitigate algoritmic bias and evaluate fairness in healthcare.
However, health inequality remains a complex issue. In this lecure, we describe a fusion-based methodology for bias mitigation in medical data.
Furthermore, we explore the relationships between demographic and clinical features and model bias, thereby adding and explainability dimension to the proposed bias mitigation strategies.
Considering direct real-world applications allows us to address the complex issues of healthcare inequality, a problem that transcends across different demographics and disproportionally impacts marginalized communities.
This is a joint event, hosted by the Dean’s Distinguished Women in Mathematics, Statistics, and Computer Science Lecture Series and the Applied Mathematics Distinguished Lecture Series.