Understanding Bias & Accuracy

AI outputs reflect the biases in their training data. Learn to identify six common bias types, understand how they manifest, and develop strategies to detect and mitigate them.

Bias Detection Exercise Bias Type Explorer

Bias Detection Exercise

Read each AI-generated output, identify the bias type, highlight where it appears, and suggest how to make it more balanced.

Exercise 1 / 6

Selection Bias

What type of bias is present?

Where in the text does the bias appear?

How would a more balanced version read?

Bias Type Explorer

Interactive reference cards for each bias type, plus an 8-question quiz to test your detection skills.

Key Insight

AI bias is not malicious intent — it is a reflection of patterns in training data and the assumptions embedded in model design. Your role as an AI user is to be the critical filter: question whose perspective is represented, what data was included or excluded, and whether the output would hold up under scrutiny from diverse viewpoints.

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