01
Source hierarchy
Primary text first. Search tools are retrieval aids, not authorities.
- Use current statutes, regulations, rules, practice directions and full decisions.
- Treat snippets, headnotes, AI summaries and commentary as leads until the primary text is checked.
- Record source ID, version or retrieval date, pinpoint, query and inclusion decision.
- Keep adverse authorities and negative findings.
- Label participant account, sourced fact, system inference and unresolved question separately.
02
Data minimization
A person should not have to publish a private case file to prove the access problem.
Public journeys remove names, contacts, medical information, employer identity, file numbers and distinctive facts. They preserve only the causal sequence needed to understand the barrier.
The prototype will collect the minimum needed for routing. A funded inference layer needs documented retention, encryption, access logging, deletion controls and a threat model before handling private facts.
03
Evaluation before claims
A fluent answer is not the success metric.
- Citation validity: does the source and pinpoint support the proposition?
- Retrieval coverage: which reviewed decisions were found and which were missed?
- Difference detection: does the output identify facts that weaken the analogy?
- Procedural accuracy: is the rule tied to the correct forum and version?
- Abstention: does the system ask for missing facts instead of filling gaps?
- User comprehension: can a stressed user understand and export the result?
- Equity and language: does quality change across languages and fact patterns?
04
Corrections
Material changes stay visible.
Each output has an update date. A material error receives a dated correction naming the old claim, the new source and the effect. Corpus builds receive version identifiers so an earlier search can be reproduced.
05
Human role
The product organizes research; it does not decide the case.
The exported pack should make later work with a lawyer, advocate, clinic or institution more productive. Yale and Stanford point toward narrow tools, partner distribution, user research and measurable quality rather than a general chatbot presented as a lawyer.
SourcesYale NewsStanford Legal Design LabStanford Legal Design Lab
06
Evidence record
Method references
This protocol will be versioned as the corpus, evaluation set and privacy architecture are implemented.
- Rules of Practice and Procedure
BC Human Rights Tribunal · Effective May 1, 2026
Current procedural rules on complaint facts, deadlines, disclosure and legal authorities.
- Terms of Use
CanLII · Updated June 3, 2026
Systematic downloading is prohibited; automated or large-scale retrieval is directed to original sources or authorized channels. Search+ availability, continuity and preservation are not warranted, and the service may be changed, suspended or discontinued.
- Privacy Policy
CanLII · Accessed October 13, 2026
Confidentiality is not guaranteed for Search+ inputs.
- Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools
Stanford Law School · 2025
Preregistered evaluation reporting hallucinations above 17% for tested LexisNexis and Thomson Reuters tools.
- Quality Metrics for AI Legal Help
Stanford Legal Design Lab · Accessed October 13, 2026
Accuracy, source quality, format, bias and informed use.
- Yale Law School’s AI lab
Yale News · November 12, 2025
Lab mission, formal-reasoning approach, legal-aid distribution and Scott Shapiro’s “AI or nothing” statement.
- AI + Access to Justice Initiative
Stanford Legal Design Lab · Accessed October 13, 2026
User research, task mapping, pilots, benchmarks, evaluation and shared infrastructure.