Reimagining Legal Fact Verification with GenAI: Toward Effective Human-AI Collaboration

Paper Title

Reimagining Legal Fact Verification with GenAI: Toward Effective Human-AI Collaboration

Publication Info

• Topic area: Human-AI collaboration in legal fact verification.
• Keywords: Legal fact verification, GenAI, human-AI collaboration, accountability, transparency, legal technology, professional judgment, automation, high-stakes domains, epistemic alignment.

Background and Problem

• Problem / challenge: Existing legal technology tools fail to support complex, judgment-dependent tasks like fact verification, which require synthesis across fragmented information sources and interpretive reasoning. GenAI's potential in this domain remains largely unexplored.
• Significance: Fact verification is critical in non-litigation legal practice, underpinning high-stakes decisions such as regulatory compliance, contractual commitments, and advisory services. Errors can lead to severe financial, reputational, and legal consequences.
• Motivation and related work: Prior research has focused on automation for specific subtasks and GenAI applications in drafting and research, but has not addressed how GenAI can assist in dynamic, interpretive workflows like fact verification. Human-AI collaboration in high-stakes domains highlights the need for systems that augment rather than replace professional judgment.

Solution

• Proposed approach: Design insights for integrating GenAI into legal fact verification workflows, emphasizing transparency, accountability, and human oversight.
• Novelty:
  1. Identification of GenAI’s role in reducing cognitive burden while preserving professional judgment.
  2. Design requirements for transparent and auditable AI systems tailored to legal fact verification.
  3. Exploration of epistemic alignment and specialization to meet legal standards.
• Procedure and key techniques:
  • Conducted semi-structured interviews with 18 non-litigation lawyers to understand current practices, challenges, and expectations for GenAI.
  • Thematic analysis of interview data to identify workflows, complexities, and envisioned future collaboration models.
  • Proposed mechanisms for transparency, automation, and specialization in GenAI systems.

Results

• Concrete findings:
  • GenAI supports cognitive orientation in unfamiliar domains, document drafting, and early-stage structuring of verification tasks.
  • Lawyers use GenAI selectively due to concerns about accuracy, confidentiality, and liability.
  • Transparency, traceability, and specialization are critical for aligning GenAI with legal epistemic norms.
• Advantage over baselines: GenAI reduces preparatory workload and cognitive overhead but requires careful integration to avoid introducing new risks or undermining professional accountability.
• Experiments / evaluation: Semi-structured interviews with 18 lawyers, thematic analysis of workflows, challenges, and expectations for GenAI in legal fact verification.
• Limitations and future work:
  • Self-reported data may not fully capture enacted practices; observational studies could complement findings.
  • Limited engagement with cross-domain comparisons; future work could explore GenAI adoption in other high-stakes fields.
  • Focused on non-litigation; extending research to litigation contexts could reveal domain-specific differences.

Summary

This study investigates how GenAI can support legal fact verification, a complex and judgment-dependent task in non-litigation practice. Through interviews with 18 lawyers, the research identifies GenAI’s potential to reduce cognitive burden in preparatory tasks while highlighting critical challenges related to accuracy, confidentiality, and accountability. The findings emphasize the need for transparent, auditable, and specialized AI systems that align with legal epistemic norms and preserve professional oversight. By addressing these requirements, the study contributes to the design of responsible AI tools for high-stakes legal workflows and advances broader HCI research on expert–AI collaboration.