Bias vs. Fairness vs. Explainability in AI

Over the last few years, there has been a distinct focus on building machine learning systems that are, in some way, responsible and ethical. The terms “Bias,” “Fairness,” and “Explainability” come up all over the place but their definitions are usually pretty fuzzy and they are widely misunderstood to mean the same thing. This blog aims to clear that up.

Bias

Before we look at how bias appears in machine learning, let’s start with the dictionary definition for the word:

“Inclination or prejudice for or against one person or group, especially in a way considered to be unfair.”

Look! The definition of bias includes the word “unfair.” It’s easy to see why the terms bias and fairness get confused for each other a lot.

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