For teams checking whether an AI system treats groups differently, get help choosing fairness measures and interpreting results such as false-positive rates by demographic group. Compare mitigation options, including reweighting data or adjusting decision thresholds, and plan the next evaluation. Leave with a bias assessment checklist, a measurement plan, and concrete questions for your model or data team.
Bias
Assess demographic bias in an AI dataset and plan fairness checks
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What it writes
- Fairness metric selection checklist
- Group-by-group evaluation plan
- Bias mitigation options comparison
How to use it
Include the model’s decision task, the groups you need to compare, and any group-level approval or error rates. That lets you get a more relevant metric plan and mitigation comparison.
What you bring
- Model task and intended users
- Group-level outcome or error rates
- Known dataset gaps and collection method
- Know which decision the model supports
A good fit for
- Reviewing a hiring model’s different pass rates across demographic groups
- Checking loan approval data before training a classification model
- Planning fairness tests after a facial recognition pilot shows uneven errors
Ways to start
A few requests this Agent can handle.
Our hiring model advances 42% of men and 28% of women. What fairness measures should we check before changing the threshold?
I have 2,000 loan applications, including 300 from one age group. How should I compare approval and error rates?
Our face matching pilot has higher false positives for darker skin tones. Help me plan an evaluation and compare mitigation options.
Good to know
This workspace provides text assistance.
- Auditing a live model without test results
- Replacing legal review of discrimination claims
- Cannot inspect datasets or run model fairness tests
- Recommendations depend on the metrics and results you paste
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What am I buying?
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Can I use the response in my work?
Use it as a draft to review and adapt. Verify calculations, facts, rights, and suitability before relying on it. Do not provide information you do not have permission to share.
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