Lesson 55 of 1550
Bias Auditing in LLM Outputs: Seeing What the Model Can't
LLMs inherit the skews of their training data and RLHF feedback. Auditing for bias isn't a one-time test — it's an ongoing practice that belongs in every deployment.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1Why LLMs produce biased outputs
- 2bias audit
- 3representational harm
- 4allocation harm
Concept cluster
Terms to connect while reading
Section 1
Why LLMs produce biased outputs
A language model trained on human text inherits human patterns — including the inequitable ones. When a model more often describes nurses as female and CEOs as male, it's not malfunctioning; it's accurately reflecting a skewed corpus. The question for deployers isn't 'is the model biased?' (it is), but 'which biases matter for my use case, and how bad are they?'
Two types of harm to measure
- Representational harm: the model degrades or stereotypes a group — describing a doctor as 'surprisingly articulate for a [group]'.
- Allocation harm: the model differentially distributes resources or opportunities — ranking resumes lower for names statistically associated with minority groups.
Building an audit set
- 1Define the task scope — a customer-service bot and a hiring tool need different audit dimensions.
- 2Create paired prompts that vary only a protected attribute.
- 3Run them in batches and record outputs verbatim — don't paraphrase.
- 4Have evaluators score outputs independently before comparing notes.
- 5Document your methodology so it can be repeated next quarter.
Benchmark limitations
Published benchmarks like WinoBias or BBQ are useful starting points but were designed for researchers, not deployers. A model that aces WinoBias may still produce biased medical advice for a patient population not represented in the benchmark. Supplement standard benchmarks with domain-specific probes you write yourself.
Key terms in this lesson
The big idea: bias auditing is a practice, not a test. Define the harm types relevant to your use case, build reproducible audit sets, and run them on every material change.
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