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 blog series, we’ll investigate how we can better test machine learning applications. In the first post, we’ll look at what we mean by ML testing, what an ML bug is, and where they occur, as well as introduce the first technique for your ML testing repertoire: regression testing.

The topic of bias in ML systems has received significant attention recently. And rightly so. The core input to ML systems is data. And data is biased due to a variety of factors. Building a system free of bias is challenging. And in fact, the ML community has long struggled to define what a bias-free or fair system is.

The recent weeks have highlighted the increasing concerns over AI safety and security and showcased a collaborative effort among global entities in the EU, US, and the UK aiming to mitigate these risks. Here's a brief overview of the most recent key regulatory developments and their potential implications for businesses.

Developed with MITRE ATLAS in mind, Lakera acts as a robust LLM gateaway, addressing vulnerabilities in data, models, and on the user front, protecting your AI applications against the most prominent LLM threats.