Detecting bias in ML models

At this point in the book, we’ve covered many of the useful, interesting, and impressive aspects of large vision and language models. Hopefully, some of my passion for this space has started rubbing off on you, and you’re beginning to realize why this is as much of an art as it is a science. Creating cutting-edge ML models takes courage. Risk is inherently part of the process; you hope a given avenue will pay off, but until you’ve followed the track all the way to the end, you can’t be positive. Study helps, as does discussion with experts to try to validate your designs ahead of time, but personal experience ends up being the most successful tool in your toolbelt.

This entire chapter is dedicated to possibly the most significant Achilles heel in ML and artificial intelligence (AI): bias. Notably, here we are most interested in bias toward and against specific groups of human beings. You’ve probably already heard about...