# Bias & Fairness (LLM-as-Judge)

- **Severity:** Medium
- **Signature ID:** `netzilo-llmjudge-bias-and-fairness-001`
- **OWASP:** LLM09 (Misinformation)

## Summary

This detection uses a secondary AI model to review the outputs of your primary AI system and flag content that looks biased or unfair. It is a monitoring aid, not a security control - it never blocks anything, it just reports what it finds.

## How the attack works

An AI agent produces a response to a user or task. That response is passed to a separate AI scanner along with a rubric asking it to judge whether the content contains biased or unfair language. The scanner returns a verdict, which is logged for review. No action is taken automatically - the rule only ever reports, at most low severity.

## Why it matters

Without this kind of check, an organization may not notice that its AI outputs are producing biased, discriminatory, or unfair content until a user complains or it becomes a public incident. This is a detection aid for that gap, not a fix - it does not stop the biased output from being generated or sent.

## What you can do

- Review flagged outputs manually before assuming they are genuine bias rather than legitimate discussion of a sensitive topic (e.g., security research, documentation, or test data mentioning protected characteristics).
- Confirm your AI scanner/judge model is actually configured and reachable - this check fails open (allows silently) if no AI keys are set up, so a misconfiguration looks like a clean bill of health.
- Track flagged-content trends over time rather than reacting to single flags, since the judge model's own bias or error rate will produce some false positives.
- Pair this with human review processes for high-stakes or customer-facing AI outputs, since this control only reports and never blocks.

## Known benign look-alikes

- Legitimate content discussing the flagged topic (security research, documentation, test data)
- AI scanner unavailable or no AI keys configured (fails open -> allow)

## References

- https://docs.smith.langchain.com/evaluation

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Source: https://www.netzilo.com/threats/llm-as-judge-bias-and-fairness