Developing Equitable AI Diagnostics: A Technical Approach to Bias Mitigation

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Topic: "Developing Equitable AI Diagnostics: A Technical Approach to Bias Mitigation"

In this talk, we will explore the critical need for fairness in AI-driven healthcare, with a focus on mitigating bias in machine learning models. As AI systems become more integrated into healthcare diagnostics, addressing the disparities in model performance across diverse ethnic groups is paramount. This session will present a technical deep dive into the challenges of bias in medical imaging datasets and the resulting impact on healthcare outcomes for underrepresented populations.

We will begin by defining the types of bias commonly found in machine learning models, with a case study in skin cancer detection. We will demonstrate how training on imbalanced datasets exacerbates disparities in diagnosing skin cancer across different racial groups. Attendees will gain insight into practical techniques for rectifying these biases, including data augmentation, fairness-aware algorithms, and advanced evaluation metrics designed to assess model equity.

In addition to discussing technical solutions, we will also address the limitations and ethical considerations surrounding bias mitigation in healthcare AI, highlighting the importance of interdisciplinary collaboration in creating equitable diagnostic tools. By the end of the session, participants will be equipped with the knowledge to implement fairness techniques in their own AI models, promoting better outcomes for all patient populations.

Speaker's bio: Laura Montoya, Founder and Managing Partner of Accel Impact Organizations

Laura is a tech leader focused on social impact and ethical AI. She founded Accel Impact Organizations, including Accel AI Institute and LXAI. With a background in biology, physics, and human development, Laura has worked at top tech companies like Intuit and has been a leader in tech diversity initiatives. She's a frequent speaker at industry conferences and has been featured in major publications.

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