Bias and Fairness Review

Why bias review matters for agents

Agents make decisions or generate content that affects people: hiring recommendations, support triage, content moderation, loan screening, and more. If the model, the data, or the retrieval source is biased, the agent will reproduce that bias at scale.

Common sources of bias

• Training data skew. Models trained on data that underrepresents some groups.
• Retrieval bias. RAG systems retrieve documents that reinforce a single perspective.
• Prompt framing. Leading questions or assumptions in the prompt shape output.
• Evaluation bias. Test sets that do not reflect the diversity of real users.
• Feedback loops. Agent output becomes training data, amplifying early mistakes.

How to review

1. Define fairness goals for your use case. What does fair output look like?
2. Test with diverse inputs. Include names, languages, regions, and edge cases.
3. Audit retrieval sources. Check whether retrieved documents represent balanced viewpoints.
4. Review outputs for stereotypes. Flag gendered, racial, age-based, or ability-based assumptions.
5. Measure outcome parity. For decision-making agents, compare outcomes across groups.
6. Create an escalation path. Route questionable outputs to a human reviewer.

Quick wins

• Add a bias check prompt to your evaluation set.
• Require human review for high-stakes recommendations.
• Document the known limitations of your model and data sources.
• Retrain or fine-tune on more representative data when gaps are found.

Related

• Red-Teaming Agents
• Output Validation for Agent Tool Calls