AI security blog

In this second instance of the testing blog series, we deep dive into data bugs: what do they look like, and how can you use specification and testing to ensure you have the right data for the job?

As part of our series on machine learning testing, we are looking at metamorphic relations. We’ll discuss what they are, how they are used in traditional software testing, what role they play in ML more broadly and lastly, how to use them to write great tests for your machine learning application.

In this instance of our ML testing series, we discuss fuzz testing. We discuss what it is, how it works, and how it can be used to stress test machine learning systems to gain confidence before going to production.

Testing machine learning systems is currently more of an art form than a standardized engineering practice. This is particularly problematic for machine learning in mission-critical contexts. This article summarizes three steps from our ML testing series that any development team can take when testing their ML systems.

AI (artificial intelligence) is capable of helping the world scale solutions to our biggest challenges but if you haven’t experienced or heard about AI’s mishaps then you’ve been living under a rock. Coded bias, unreliable hospital systems and dangerous robots have littered headlines over the past few years.

“Data is a reflection of the inequalities that exist in the world”. While this might be true, developers have great potential to curb bias in their computer vision systems.