Summary

You might have heard the saying garbage in, garbage out when it comes to data and AI. In this chapter, you saw that we should also take just as seriously the phrase bias in, bias out.

We looked at some of the primary areas where bias can creep into our data and saw that we must start with an eye toward finding this sooner rather than later. At certain points in the process, it's too late. Bias and discrimination can have real-world impacts, from hiring and vehicle safety to continuing unjust practices around social norms.

You have a few options to make sure that you are doing all you can to avoid this bias such as having the domain knowledge or consulting those who do and getting others from different backgrounds to look at data (or better yet, on your team).

There are also many other types of bias that exist out there and, admittingly, things that we don't even realize are areas of concern. It's also important to be aware that the drift talked about...