Advances in AI are changing how we deliver healthcare services, how companies recruit and hire, how we shop online, how we police and administer justice, and just about everything in between. But the more we use AI to power and automate crucial parts of our daily lives, the more we need to be able to trust that these models are accurate, equitable, and high-performing.

That’s why we’ve created a deep dive piece that explores the four major categories of bias that can affect AI/ML models. In this piece we cover:

• Distinguish between the four types of bias,
• Explain the difference between bias in your training data versus bias in your algorithm,
• Sketch out the basic ways that experienced ML/AI teams work to mitigate these potential sources of bias.