Sign In

Communications of the ACM


Fragility in AIs Using Artificial Neural Networks

line-connected colored dots, illustration

Credit: RamCreative

Artificial neural networks (ANNs) are a promising technology for supporting decision making and autonomous behavior. However, most current-day ANNs suffer from a fault that hinders them from achieving their promise: fragility. Fragile AIs can easily make faulty recommendations and decisions, and even execute faulty behavior, so reducing ANN fragility is highly desirable. In this Viewpoint, we describe issues involved in resolving ANN fragility and possible ways to do so. Our analysis is based on what is known about natural neural networks (NNNs), that is, animal nervous systems, as well as what is known about ANNs.

Back to Top

What Is Fragility?

Engineered systems fall on a continuum from robust to fragile. One reason some ANNs are considered "fragile" is that seemingly minor changes in the data they are given can cause major shifts in how they classify the data. Such fragility is often evident when lab-trained ANNs are finally tested under real-world conditions. A classic example of hyper-fragility is an ANN that, after being trained to recognize images of stop signs, fails to recognize ones in which a small percentage of pixels has been altered.


No entries found

Log in to Read the Full Article

Sign In

Sign in using your ACM Web Account username and password to access premium content if you are an ACM member, Communications subscriber or Digital Library subscriber.

Need Access?

Please select one of the options below for access to premium content and features.

Create a Web Account

If you are already an ACM member, Communications subscriber, or Digital Library subscriber, please set up a web account to access premium content on this site.

Join the ACM

Become a member to take full advantage of ACM's outstanding computing information resources, networking opportunities, and other benefits.

Subscribe to Communications of the ACM Magazine

Get full access to 50+ years of CACM content and receive the print version of the magazine monthly.

Purchase the Article

Non-members can purchase this article or a copy of the magazine in which it appears.
Sign In for Full Access
» Forgot Password? » Create an ACM Web Account