Why bias spotting matters
AI models reflect training data biases and optimize for fluency, not truth. A confident-sounding output can embed stereotypes, omit edge cases, or anchor on the first example it saw. Your job is to catch these before stakeholders do.
The 7-Point Bias Checklist
Run these checks on every AI output that will influence decisions:
1. Source Diversity Check
Ask: Does the output reference multiple perspectives, or does it lean on a single viewpoint?
Red flag: All examples come from one industry, region, or demographic.
Fix: Prompt for "alternative viewpoints" or "counterexamples from different contexts."
2. Anchoring Detection
Ask: Is the output overly influenced by the first example or constraint I provided?
Red flag: The response mirrors your initial framing too closely without pushback.
Fix: Re-run with a different opening example and compare outputs.
3. Confirmation Bias Scan
Ask: Does the output only support my hypothesis, or does it present contrary evidence?
Red flag: No "however," "alternatively," or "risks include" language.
Fix: Explicitly ask: "What evidence would contradict this conclusion?"
4. Omission Audit
Ask: What stakeholders, edge cases, or failure modes are missing?
Red flag: Output addresses happy path only; no mention of exceptions.
Fix: Prompt: "List three scenarios where this approach would fail."
5. Recency Bias Check
Ask: Is the output weighting recent events disproportionately?
Red flag: Analysis overfits to last quarter's data or recent headlines.
Fix: Ask for "historical patterns" or "long-term trends" explicitly.
6. Authority Bias Detection
Ask: Does the output cite sources, or does it assert authority without evidence?
Red flag: Phrases like "research shows" or "experts agree" without citations.
Fix: Demand specific sources: "Cite three peer-reviewed studies supporting this claim."
7. Stereotyping Scan
Ask: Does the output make assumptions about groups based on categories?
Red flag: Generalizations about roles, demographics, or industries without qualifiers.
Fix: Replace categorical statements with "some," "in certain contexts," or specific data.
- ✓Source diversity: Multiple perspectives represented?
- ✓Anchoring: Output differs from your initial framing?
- ✓Confirmation: Contrary evidence included?
- ✓Omissions: Edge cases and failures addressed?
- ✓Recency: Historical context balanced with recent data?
- ✓Authority: Claims backed by specific sources?
- ✓Stereotypes: Group generalizations qualified?
When to Use This Checklist
- Before sharing any AI-drafted strategy document
- Before presenting AI-generated analysis to leadership
- Before publishing AI-assisted content externally
- When AI output will inform hiring, pricing, or policy decisions
“"We caught a regional bias in our market analysis that would have embarrassed us in the board meeting. Ten minutes of checking saved a week of damage control."”
Related Resources
- Evidence Levels explained — understand what "B-level evidence" means
- Red-Teaming in Five Minutes — adversarial prompts to stress-test outputs
- Evaluator Rubrics — lock your quality criteria before reviewing
Apply this now
Practice prompt
Take an AI output from this week and run all seven bias checks. Note which checks flag issues.
Try this now
Run the Judgment Pack and apply this checklist to the sample outputs.
Common pitfall
Skipping the omission audit—teams catch obvious bias but miss what the AI didn't mention.
Key takeaways
- •Run all seven checks on high-stakes deliverables—takes under 10 minutes
- •Confirmation bias and anchoring are the most common failure modes
- •Demand specific sources when AI asserts authority
See it in action
Drop this into a measured run—demo it, then tie it back to your methodology.
See also
Pair this play with related resources, methodology notes, or quickstarts.
Further reading
Next Steps
Ready to measure your AI impact? Start with a quick demo to see your Overestimation Δ and cognitive load metrics.