Towards responsible deployment of robust and private AI models in healthcare

Add to your list(s) Download to your calendar using vCal

• Olivia Wiles, DeepMind
• Tuesday 28 January 2025, 16:00-17:00
• Computer Lab, FW26 and Online.

If you have a question about this talk, please contact Cecilia Mascolo.

Zoom: https://cam-ac-uk.zoom.us/j/89567921468?pwd=y0ad2zTt8hcBLg7dwPKAtjNdcJkd5f.1

Bio: Olivia Wiles is a Staff Research Scientist at DeepMind working on robustness and evaluation of large models in machine learning, focussing on application driven research ranging from medical applications to large, multimodal foundational models. Prior to this, she was a PhD student at Oxford with Andrew Zisserman studying self-supervised representations for 3D.

Abstract: AI breakthroughs for medical applications are happening at pace, but it is important to consider how to ensure trustworthiness of these solutions before adoption. While true for ML as a whole , these questions are especially vital in the medical domain. I will discuss two angles of trustworthiness—fairness/robustness and privacy—and how we can build solutions that aim to ensure these requirements are met from the ground up by leveraging generative models. While these approaches show promising results, they are not a panacea, and a holistic approach is required to identify and mitigate challenges for deploying AI solutions in medical applications.

This talk is part of the Mobile and Wearable Health Seminar Series series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge talks
• Computer Lab, FW26 and Online
• Department of Computer Science and Technology talks and seminars
• Interested Talks
• Mobile and Wearable Health Seminar Series
• School of Technology
• Trust & Technology Initiative - interesting events
• bld31

Note that ex-directory lists are not shown.