Interpretable Neural Networks for Computer Vision: Clinical Decisions | AI FOR GOOD DISCOVERY

Let us consider a difficult computer vision challenge. Would you want an algorithm to determine whether you should get a biopsy, based on an x-ray? That’s usually a decision made by a radiologist, based on years of training. We know that algorithms haven’t worked perfectly for a multitude of other computer vision applications, and biopsy decisions are harder than just about any other application of computer vision that we typically consider. The interesting question is whether it is possible that an algorithm could be a true partner to a physician, rather than making the decision on its own. To do this, at the very least, we would need an interpretable neural network that is as accurate as its black box counterparts. In this talk, I will discuss two approaches to interpretable neural networks: (1) case-based reasoning, where parts of images are compared to other parts of prototypical images for each class, and (2) neural disentanglement, using a technique called concept whitening. The case-based reasoning technique is strictly better than saliency maps, and the concept whitening technique provides a strict advantage over the posthoc use of concept vectors.

In Partnership with: @fraunhofer HHI

🎙 Speaker:
Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, and Statistical Science, Duke University

🎙 Moderator:
Wojciech Samek, Head of Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute

🔴 Watch the latest #AIforGood videos!

Explore more #AIforGood content:
1️⃣ AI for Good Top Hits

2️⃣ AI for Good Webinars

3️⃣ AI for Good Keynotes

📩 Stay updated and join our weekly AI for Good newsletter:

📅 Discover what's next on our programme!

🗞Check out the latest AI for Good news:

📱Explore the AI for Good blog:

🌎 Connect on our social media:

What is AI for Good?
The AI for Good series is the leading action-oriented, global & inclusive United Nations platform on AI. The Summit is organized all year, always online, in Geneva by the ITU with XPRIZE Foundation in partnership with over 35 sister United Nations agencies, Switzerland and ACM. The goal is to identify practical applications of AI and scale those solutions for global impact.

Disclaimer:
The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.

#AIforGoodDiscovery #TrustworthyAI