IBIIS & AIMI Seminar: ‘Fair’ AI Modeling for Digital Healthcare

Imon Banerjee, PhD
Associate Professor of Radiology
Director of AI Innovation Hub
Mayo Clinic, Arizona
Title: ‘Fair’ AI Modeling for Digital Healthcare
Abstract:
With the rapid advancements in AI models, there has been growing concern in recent literature about the risk of unintended biases affecting individuals unfairly based on factors like race, gender, and other clinical characteristics. One of the key challenges in analyzing AI bias is that the factors contributing to unfairness are often interconnected and can amplify one another. It is crucial to understand how individual, institutional, and societal biases may be either mitigated or exacerbated when adopting AI systems. Common approaches to eliminating bias, such as developing demographic-specific models, often face the challenge of insufficient demographic representation. In this talk, we present an alternative computational approach to creating ‘fair’ models that aim to reduce bias without relying on demographically balanced datasets. Our observations indicate that techniques aimed at decoupling demographic information from task predictions typically fail to match the performance of baseline models. While preserving the model performance, we will explore a novel ‘fair’ AI modeling approach that incorporates adversarial, causal, and contrastive learning techniques, with a focus on applications in digital healthcare.