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Understanding Generative AI Hallucinations in Legal Discovery

A wooden gavel resting on a New York court brief that is dissolving into red and white digital pixels, symbolizing the risks of generative AI hallucinations and fabricated metadata in legal discovery.

By Matt Zhun, RGAIP | Vice President of Sales, Trustpoint.One

Abstract

Generative AI (GenAI) hallucinations pose significant risks in legal discovery, where accuracy and verifiability are paramount. This article examines the mechanisms behind GenAI hallucinations, particularly in handling metadata during e-discovery processes. Drawing on real-world cases, such as Mata v. Avianca, Inc., and emerging incidents, we explore how these errors arise from model limitations and their implications for legal practice. Mitigation strategies, including Retrieval-Augmented Generation (RAG) and human oversight, are discussed, alongside evolving regulatory frameworks like the ABA guidelines and EU AI Act. By analyzing these elements, this study underscores the need for disciplined integration of AI to maintain evidentiary integrity.

Introduction

The first widely documented and notorious case of AI hallucinations in a legal filing was Mata v. Avianca, Inc. in May 2023. In this case, an attorney representing the plaintiff in a personal injury lawsuit used the generative AI tool ChatGPT to assist in legal research. The resulting court brief contained citations to at least six entirely nonexistent judicial decisions, complete with fake case names, citations, and fabricated internal quotations and summaries. The opposing counsel and the judge were unable to locate the cited cases. When confronted, the attorney admitted to using AI and failing to verify the information it provided. The judge in the U.S. District Court for the Southern District of New York described the circumstance as “unprecedented” and subsequently fined the lawyers $5,000 involved for violating their duty of candor to the court. This incident brought widespread attention to the risks of using unverified AI-generated content in legal practice and served as a prominent cautionary tale for the legal profession. Still, hardly a week goes by where we don’t see similar incidents occurring in the legal community.

GenAI “hallucinations” refer to instances where the model generates plausible sounding but factually incorrect or fabricated information. This isn’t intentional deception; it’s a byproduct of how large language models (LLMs) like GPT variants or legal-specific tools (e.g., Harvey, Casetext’s CoCounsel) predict and synthesize output based on patterns in training data, rather than true reasoning or verification. In the context of legal discovery, the pretrial phase where parties exchange relevant evidence, documents, and data, hallucinations are particularly risky because discovery relies on accuracy, authenticity, and chain-of-custody for admissibility in court.

Metadata (e.g., timestamps, author names, file versions, geolocation, edit histories) is a critical subset of this evidence. It is often “invisible” data embedded in files (like PDFs, emails, or Word docs) that provides context, origin, and integrity. When GenAI hallucinates metadata during discovery workflows, it can undermine cases, trigger sanctions, or erode trust in automated tools. Below, we break down how this happens, with mechanisms, examples, and implications.

Mechanisms of GenAI Hallucinations in Legal Discovery

GenAI tools are increasingly used in e-discovery for tasks like document review, summarization, redaction, and metadata extraction. Hallucinations arise from inherent model limitations, amplified by the high-stakes, unstructured nature of legal data. Here’s how it unfolds:

  1. Pattern-Based Inference Without Grounding:
    • How it works: GenAI doesn’t “know” facts; it completes prompts by statistically predicting tokens (words/sub words) based on training corpora. If the input document has incomplete, ambiguous, or missing metadata (common in scanned PDFs or legacy files), the model fills gaps with “likely” details drawn from similar patterns in its training data.
    • Metadata trigger: For instance, when extracting author fields from emails, if the “From” line is corrupted or absent, the AI might infer an author like “John Doe, Esq.” because it’s a common name in legal datasets, even if the real sender was “Jane Smith.”
    • In discovery: Tools like Relativity or Everlaw integrate GenAI for bulk processing; unverified outputs can propagate errors across thousands of documents.
  2. Overgeneralization from Training Data Biases:
    • How it works: LLMs are trained on vast internet-scraped data, including legal filings, which often feature standardized formats (e.g., “Created: [YYYY-MM-DD]”). But biases creep in—e.g., overrepresentation of U.S. East Coast timestamps—leading to fabricated alignments.
    • Metadata trigger: A document from a 2023 merger might get hallucinated as “Modified: March 15, 2024” if the model confuses it with post-merger filings in its training set, ignoring the actual EXIF data.
    • In discovery: In privilege reviews, hallucinated edit histories could falsely flag a doc as “non-privileged” (e.g., inventing a “shared with counsel” timestamp), exposing sensitive info.
  3. Prompt Engineering and Contextual Drift:
    • How it works: Prompts guide the AI (e.g., “Extract metadata from this contract PDF”). If the prompt is vague or the context window overflows with multiple files, the model “drifts” and blends details across documents.
    • Metadata trigger: Processing a chain of emails, the AI might attribute a reply’s timestamp to the original message, hallucinating a “Sent: 2025-01-15 at 14:32 UTC” for a 2024 thread, based on averaged patterns.
    • In discovery: During TAR (Technology-Assisted Review), where AI ranks documents for relevance, cross-contamination can mislabel metadata, skewing search results and delaying production.
  4. Multimodal or Hybrid Processing Errors:
    • How it works: Advanced GenAI (e.g., vision-language models like GPT-4V) analyzes images/scans of docs, apply OCR text and inferring metadata. Errors compound if OCR fails, leading to invented fills.
    • Metadata trigger: A blurry scan of a signed agreement might hallucinate “Signed by: [Prominent CEO name from news]” with a fake geolocation, pulling from correlated training examples.
    • In discovery: Non-digital natives (e.g., handwritten notes) are common; hallucinations here can fabricate chains of custody, invalidating evidence under FRE 901 (authentication rules).
  5. Fine-Tuning and Domain Adaptation Flaws:
    • How it works: Legal-tuned models (e.g., Lexis+ AI) are fine-tuned on case law and contracts, but if the fine-tuning dataset lacks diverse metadata (e.g., underrepresented international filings), the model defaults to hallucinations.
    • Metadata trigger: In antitrust discovery, a global cartel doc might get a U.S.-centric “Jurisdiction: Delaware” tag hallucinated, ignoring actual EU origins.
    • In discovery: Cost-saving automation scales this across terabytes, but without human QA, errors compound in FRCP 26(g) certifications of completeness.

Real-World Examples and Cases

  • Mata v. Avianca, Inc. (Extended): Building on the 2023 aviation case where ChatGPT fabricated citations, subsequent analyses and follow-ups in 2025 highlighted ongoing issues with AI-assisted review, including invented “access logs” for documents in related proceedings.
  • FTC v. Amazon Antitrust Probes: In the ongoing antitrust case, while no specific discovery glitches involving AI hallucinations were reported, the case has spotlighted AI’s role in algorithmic pricing, raising broader concerns about AI reliability in high-stakes discovery. Similar issues have echoed in other 2025 antitrust probes where AI tools were implicated in document handling errors.
  • Industry Benchmarks: Recent reports, including those from Relativity, note hallucination rates in metadata extraction for unstructured data, with studies indicating rates as high as 17-33% in legal AI tools, per evaluations like Stanford’s assessments. ILTA surveys and compiled databases of hallucination cases further document a rise in incidents.

Implications for Legal Practice

  • Evidentiary Risks: Fabricated metadata can lead to spoliation claims, sanctions (e.g., under FRCP 37(e)), or exclusion of evidence. Courts increasingly scrutinize AI use (e.g., 2024-2025 ABA guidelines mandate disclosure).
  • Ethical Duties: Lawyers must verify AI outputs (Model Rule 1.1 competence); blind reliance is malpractice bait.
  • Efficiency Trade-Offs: While GenAI cuts review time by 40-60%, hallucinations inflate costs for audits.

Mitigation Strategies

  • Retrieval-Augmented Generation (RAG): Ground AI in verified databases (e.g., link to original file hashes) to reduce fabrications substantially, as shown in Stanford studies where RAG lowered hallucination rates in legal tools.
  • Human-in-the-Loop: Layer spot-checks (e.g., 10% sampling) and confidence scoring (AI flags low-certainty metadata).
  • Tool-Specific Fixes: Use metadata-native tools with AI, enforcing strict extraction over generation.
  • Tool Specific Validation: Create a prioritized review in Relativity’s Review Center using aiR relevance ranking and review until there is a sharp drop in relevance rate.  Now, switch to Active Learning in the review center. This will prioritize the remaining docs using CAL rankings.  This step will fold in all the docs that aiR missed since it learned from your tagging.
  • Regulatory Shifts: By 2025, the EU AI Act and U.S. state bars require “explainability logs” for discovery AI, tracing hallucinations.

Conclusion

In summary, GenAI hallucinates metadata in legal discovery by probabilistically filling evidentiary voids with training-derived fiction, exploiting the gap between prediction and verification. This isn’t a bug to “fix” entirely, it’s architectural, but disciplined workflows can contain it.