In the fast-evolving world of public sector labor law, tools
like generative AI promise efficiency and innovation. But as a recent decision
from the Public Employment Relations Board (“PERB”) reminds us, they can also
spell disaster if not handled with the utmost care. In California State
University Employees Union v. Trustees of the California State University (San
Diego) (PERB Case No. LA-CE-1433-H), an Administrative Law Judge (“ALJ”)
took the extraordinary step of striking the employer’s pre-hearing brief from
the record. The reason? Fabricated citations and quotations from a federal
appellate decision that simply didn’t exist as presented—hallmarks of unchecked
AI output.
Let’s break this down step by step, because this isn’t just
a procedural hiccup; it’s a wake-up call for unions, employers, and
practitioners alike in California’s public safety sector.
The Case Background
The underlying dispute centers on the employee status of
residential assistants (“RAs”) under the Higher Education Employer-Employee
Relations Act (“HEERA”), specifically Government Code section 3562(e). The
union argued that student RAs qualify as employees entitled to bargaining
rights, while the Trustees of the California State University (“CSU”) contended
otherwise. In preparation for a hearing, the ALJ directed both parties to
submit pre-hearing briefs addressing the legal test for employee status and the
relevance of federal Fair Labor Standards Act (“FLSA”) precedents.
CSU filed its brief on November 3, 2025, relying heavily on Marshall
v. Regis Educational Corp. (10th Cir. 1981) 666 F.2d 1324 (“Marshall”)—a
real case, but one that CSU misrepresented through inaccurate page citations
and invented quotations. For instance, the brief claimed the Tenth Circuit held
that RAs’ duties were “primarily educational rather than economic in nature”
and that they “receive the greater benefit from the program.” In reality, Marshall
ends at page 1328 and contains none of these phrases or conclusions. The ALJ
spotted the discrepancies, issued an Order to Show Cause, and ultimately struck
the entire brief as a sanction after CSU’s response failed to adequately
explain the errors.
CSU admitted to “misnumbering of pages” and “erroneously
included quotation marks around paraphrasing statements,” attributing it to a
failure to double-check. But the ALJ wasn’t buying it, noting that the
misrepresentations went beyond mere typos—they distorted the case’s holdings in
a way that aligned suspiciously with CSU’s position. Drawing parallels to Noland
v. Land of the Free, L.P. (2025) 336 Cal.Rptr.3d 897, where a California
appellate court sanctioned counsel for AI-generated fabrications, the ALJ
emphasized that such “hallucinations” undermine the integrity of legal
proceedings.
Why This Matters for Public Safety Unions
For unions representing California’s firefighters, police
officers, corrections staff, and other public safety workers, this ruling
underscores a critical lesson: diligence in legal advocacy isn’t optional. PERB
proceedings, like those under the Meyers-Milias-Brown Act (“MMBA”) or the Dills
Act, demand precision because the stakes—bargaining rights, working conditions,
and member protections—are high. Imagine a grievance over shift differentials
or safety equipment where a union’s brief gets tossed due to sloppy AI use. Not
only does it weaken your position, but it could invite scrutiny or
countersanctions that distract from the merits.
More broadly, this decision signals PERB’s intolerance for
shortcuts in an era where AI tools like ChatGPT are tempting for drafting
research summaries or arguments. As the ALJ pointed out, citing CSU’s own AI
guidelines, it’s the attorney’s responsibility to verify content. In public
sector labor, where decisions often set precedents affecting thousands of
members, relying on unvetted AI could erode trust with arbitrators, boards, or
courts. We’ve seen similar pitfalls in federal cases, but this is one of the
first in California’s public employment arena—and it happened to a major
employer like CSU, which should know better.
The potential ripple effects? Expect heightened scrutiny of
briefs in PERB and related forums. Unions might see employers trying to exploit
AI for aggressive positions, only to backfire as in this case. On the flip
side, it empowers unions to challenge dubious citations, turning the tables in
discovery or hearings. And for ongoing debates like student employee
status—relevant if your union deals with campus safety personnel—this ruling
keeps the focus on statutory language over manufactured precedents.
Lessons Learned and Best Practices
CSU’s misstep highlights how overreliance on AI technology
can backfire. AI is a tool that must be used carefully. It’s a substitute
for human research and must be verified. Always cross-check citations, quotes,
and summaries against primary sources. Tools like Westlaw or Lexis are
irreplaceable for this. Follow the State Bar’s guidance on AI, which stresses
competence and candor. Don’t let “enhancements” from AI platforms slip through
without review. Labor organizations, and lawyers alike, should establish
protocols for AI use, including safeguards for confidential information and
verification policies. If you spot hallucinations in an opponent’s filing,
don’t hesitate to call it as doing so will strengthen your case.
