Definition

Bias in AI means the system gives results that are unfair or favor one group over another. This can happen because the training data isn’t balanced, the model wasn't tested well, or human choices during development introduced hidden rules.

Example

An AI resume filter that picks more male candidates than female ones shows bias in AI.

How It’s Used in AI

Bias shows up in hiring tools, facial recognition, healthcare, and more. It's a major concern because AI decisions can affect real lives. Developers use tools to test, fix, or reduce bias—but it's hard to remove it completely.

Brief History

Bias in AI became widely discussed in the 2010s with real-world examples of harm. High-profile studies and whistleblowers—like Timnit Gebru—brought major attention to how AI can reflect and repeat real-world inequality.

Key Tools or Models

Bias-checking tools include Fairlearn, AI Fairness 360 (IBM), and Google’s What-If Tool. Some models like Claude and GPT-4 are trained with safety layers to reduce bias in outputs.

Pro Tip

Bias isn't always obvious. Regularly test your AI on real-world examples to find blind spots—and use diverse data from the start.