Bias, Relevance, and Fit: Quality Control Beyond Accuracy
Even when an AI output is factually correct, it can still be unhelpful, biased, or misaligned with the jurisdiction, client goals, or forum expectations.
Bias checks for legal outputs
- Look for stereotypes or loaded language.
- Check whether the output assumes facts not in evidence.
- Ask the model to produce a neutral rewrite and compare.
- Have a second reviewer scan for tone and fairness.
Relevance checks
Ask: does the output answer your question, for your jurisdiction, and for your audience? If not, refine the prompt or narrow the input.
Output scoring rubric (quick)
| Dimension | What to look for |
|---|---|
| Accuracy | No fabricated citations; facts match sources. |
| Jurisdiction fit | Correct forum, governing law, and procedural posture. |
| Completeness | Key issues addressed; no major omissions. |
| Tone | Appropriate for client/court/colleague. |
| Risk | Flags uncertainties; avoids overclaiming. |
Try it
Exercise: Ask the model to summarize a case or statute you provide. Then ask:
- “List the top 5 claims you made that require verification.”
- “Rewrite the summary for a client with no legal background.”
- “Rewrite for a judge in a formal tone with citations.”
Compare differences and identify where hallucinations or bias might appear.