How do we monitor fairness without storing sensitive attributes in logs?

Log only a stable join key (e.g., user_id_hash). In a restricted analytics project, join that key to sensitive attributes under access controls, compute fairness aggregates (disparate impact, equalized odds), and export only metrics/time series. Never persist raw attributes in request logs.

What thresholds should we use for fairness?

Start with disparate impact between 0.8 and 1.25 and equalized odds gaps < 0.1 absolute, then tune to your domain and risk tolerance. Crucially, encode thresholds in code and CI/CD, not in a slide deck.

Do we need human-in-the-loop for safety?

For high-risk actions (financial advice, medical, compliance), yes—route low-confidence or unsafe outputs to review. For medium-risk flows, automated guardrails + strong fallbacks (including answering “I don’t know”) are usually sufficient.

Which tools do you recommend for a v1?

OpenTelemetry for tracing, Prometheus/Grafana for metrics, Fairlearn for fairness audits, Evidently for drift, LaunchDarkly/Unleash for flags, and a managed LLM provider with content safety APIs. Add WhyLabs/Arize/Fiddler when you need managed monitoring or cross-model comparisons.

How do we test hallucination?

Maintain a labeled QA set and compute groundedness via citation checks and spot review. Use LLM-as-judge carefully with calibration, and backstop with rule-based checks (required citations, policy keywords). Track hallucination_rate over time and gate deploys on it.

Ai-delivery · Oct 4, 2025 · 10 minute read

The Night Your LLM Went Off-Script: Shipping Bias Detection and Fairness Monitoring That Actually Works

Q: Do we need human-in-the-loop for safety?

For high-risk actions (financial advice, medical, compliance), yes—route low-confidence or unsafe outputs to review. For medium-risk flows, automated guardrails + strong fallbacks (including answering “I don’t know”) are usually sufficient.

Q: Which tools do you recommend for a v1?

OpenTelemetry for tracing, Prometheus/Grafana for metrics, Fairlearn for fairness audits, Evidently for drift, LaunchDarkly/Unleash for flags, and a managed LLM provider with content safety APIs. Add WhyLabs/Arize/Fiddler when you need managed monitoring or cross-model comparisons.

Q: How do we test hallucination?

Maintain a labeled QA set and compute groundedness via citation checks and spot review. Use LLM-as-judge carefully with calibration, and backstop with rule-based checks (required citations, policy keywords). Track hallucination_rate over time and gate deploys on it.

If you don’t instrument bias and safety like a first-class SLO, your AI will fail silently—until it fails publicly.

Alex Grant

Principal Engineer, GitPlumbers

20 years shipping distributed systems, from adtech to fintech. Ex-Netflix platform team, led SRE and ML infra at two unicorns. Now helping teams ship AI safely at GitPlumbers.

If you don’t instrument bias and safety like a first-class SLO, your AI will fail silently—until it fails publicly.

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The Night Your LLM Went Off-Script: Shipping Bias Detection and Fairness Monitoring That Actually Works

Key takeaways

Implementation checklist