LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI
arXiv:2606.18021v1 Announce Type: cross Abstract: AI systems deployed in legal workflows hallucinate at rates that aggregate metrics report at ~52%, but this average conceals where errors concentrate and in which direction they run, leaving compliance officers without an actionable signal for trustworthy deployment. We present LegalHalluLens, an auditing framework with three components: typed hallucination profiles across four legally-motivated claim categories (numeric, temporal, obligation/entitlement, factual) over CUAD (Hendrycks et al., 2021); a Risk Direction Index (RDI) that reduces omi
The proliferation of AI in legal applications necessitates robust auditing frameworks to address trust and reliability concerns as development matures beyond initial deployment to integration into critical workflows.
This development provides a crucial tool for mitigating AI hallucination risks in high-stakes legal contexts, directly addressing a primary barrier to wider trustworthy adoption of AI agents in regulated sectors.
The ability to categorize and quantify specific types of AI hallucinations in legal AI systems provides targeted actionable signals for compliance officers, moving beyond generalized error metrics.
- · Legal AI developers
- · Law firms adopting AI
- · AI auditing firms
- · Regulators
- · Untrustworthy AI systems
- · Law firms slow to adopt AI
Increased trustworthiness and accelerated adoption of AI in legal workflows.
Development of industry standards and certifications for AI reliability in legal and other regulated fields.
Shift in regulatory focus from blanket restrictions to performance-based or audited compliance for AI systems.
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Read at arXiv cs.CL