Understanding AI Bias with Simple Examples
Bias is not always intentional, and it can be measured.
Where it comes from
Data and design choices.
- Unbalanced data.
- Labels reflecting past decisions.
- Missing groups.
Why it matters
Unequal outcomes in hiring, lending or healthcare.
Reducing it
Test and monitor.
- Evaluate by group.
- Diversify data.
- Add human review.
Ask questions
Be critical.
- What data was used?
- Who was tested?
- Who is accountable?
Key takeaway: Bias comes from data and design; test results across groups and keep humans accountable.
