The Recorder, “AI Meets Paga: The Next Wave of High-Stakes Employment Litigation in California,” by Kartikey A. Pradhan, Esq. and Emma Adams, Esq., 5-15-26
Kaufman Dolowich’s San Franciso Partner Kartkey A. Pradhan and Associate Emma Adams in a recent article in The Recorder discuss how the increasing use of AI in HR can create PAGA exposure for California employers.
Read the full article below:
AI Meets PAGA: The Next Wave of High-Stakes Employment Litigation in California
With increased adoption of AI-driven workforce tools and the continued plaintiff-side focus on wage-and-hour claims, this intersection is likely to become a significant driver of PAGA litigation in the coming years.
By Kartikey A. Pradhan and Emma Adams, Kaufman Dolowich LLP
May 15, 2026
California employers are entering a new phase of employment risk. As artificial intelligence (AI) becomes embedded in core HR functions—from timekeeping to classification—traditional compliance issues are being replicated at scale. When combined with California’s Private Attorneys General Act (PAGA), which permits employees to pursue representative civil penalties for Labor Code violations, even minor systemic errors can potentially escalate into high-exposure litigation.
With increased adoption of AI-driven workforce tools and the continued plaintiff-side focus on wage-and-hour claims, this intersection is likely to become a significant driver of PAGA litigation in the coming years.
Why AI + PAGA Is a Unique Litigation Multiplier
Under PAGA (Cal. Lab. Code Sections 2698–2699.5), employees may sue as “private attorneys general” to recover civil penalties for Labor Code violations affecting other employees. AI‑driven systems by design apply uniform rules across large employee populations, automating decisions about pay, scheduling, classification, and timekeeping. To the extent those systems embed incorrect assumptions or operate without sufficient oversight, they can generate systemic, repeatable violations—precisely the type of conduct PAGA is intended to address.
AI itself is not directly regulated under PAGA; instead, AI‑driven tools can introduce or amplify traditional wage‑and‑hour and workplace‑practice violations that then become the predicate for PAGA claims.
High‑Risk AI Use Cases Under PAGA
The following examples highlight common ways AI-driven tools can create systemic wage-and-hour risks that may give rise to representative claims under PAGA:
- AI‑Driven Timekeeping and Rounding
Automated time systems that adjust punch‑in/out times, infer break compliance, or apply rounding rules can create off‑the‑clock work, missed meal or rest breaks, and inaccurate wage statements. Each pay period may constitute a separate PAGA violation. - Automated Scheduling Tools
Predictive scheduling systems that optimize staffing based on historical data may produce shortened or missed breaks across large groups of employees, generating recurring violations that are easily aggregated under PAGA. - AI‑Assisted Worker Classification
AI tools used to assess whether workers are employees or independent contractors may misapply California’s AB 5 and the ABC test first adopted by the California Supreme Court in Dynamex Operations West v. Superior Court (2018) and subsequently codified by AB 5, leading to cascading liability for unpaid overtime, missed breaks, and reimbursement claims—all classic PAGA predicates. - AI‑Enhanced Payroll and Compensation
AI‑driven systems that dynamically calculate wages, bonuses, or incentives can inadvertently trigger wage‑statement defects (Lab. Code Section 226) or delayed payments (Lab. Code Section 204), both of which are frequent gateways to PAGA actions.
Where AI Intersects With FEHA and PAGA
AI‑driven employment systems do not operate in a legal vacuum. Under the Fair Employment and Housing Act (FEHA), algorithmic decision‑making can give rise to disparate‑impact claims, particularly where automated tools fail to account for protected characteristics or individual‑accommodation needs. At the same time, those same systems may produce labor code violations such as meal and rest breaks violations, wage‑statement errors, or overtime underpayments that feed directly into PAGA exposure.
While FEHA and PAGA operate as parallel statutory frameworks, recent regulatory action by California’s Civil Rights Department—through its Civil Rights Council—reinforces that employers remain responsible for the outcomes of AI‑based HR tools, even when those systems are developed or implemented by third‑party vendors. Notably, the council has approved regulations clarifying how existing anti‑discrimination laws apply to artificial intelligence and automated decision systems in employment, with an effective date of Oct. 1, 2025.
Reasonable Steps After PAGA Reform
Following recent PAGA reforms, courts may reduce civil penalties where employers can demonstrate they took reasonable steps to comply with the law, but this standard does not eliminate underlying liability for labor code violations. In the context of AI‑driven systems, courts are likely to scrutinize:
- Pre‑deployment testing against labor code requirements
- Ongoing audits of wage‑and‑hour outcomes
- Human‑in‑the‑loop review for anomalies or complaints
- Prompt identification and remediation of system‑wide errors
Employers that can document structured compliance efforts around AI tools are likely to be in a stronger position to mitigate penalties, even where violations are found.
Discovery Risks and Emerging Plaintiff Theories
AI‑heavy PAGA cases are likely to trigger complex discovery disputes. Plaintiffs may seek access to:
- Algorithm design documentation and system logic
- Vendor communications and training‑data inputs
- Internal audit results and bias‑testing reports
These demands will often collide with employer trade‑secret and confidentiality protections, setting the stage for disputes over the scope of disclosure and the use of protective orders.
Plaintiffs are also beginning to frame AI‑driven systems as inherently risky infrastructures, advancing theories such as:
- Uniform underpayment or break violations driven by automated systems
- Failure to monitor, validate, or audit AI outputs
- Alleged constructive knowledge of violations based on employer‑access to system‑wide data
- Stacked penalties across wage statements, waiting‑time claims, and related violations
When combined with PAGA’s ability to aggregate these violations, relatively small operational flaws can be reframed as enterprise‑wide misconduct.
Defense and Compliance Strategies
Courts evaluating AI-related PAGA claims will likely focus less on the technology itself and more on whether employers implemented meaningful compliance controls around its use. Effective strategies to consider include:
- Treating AI as a compliance‑critical system, not just a convenience tool. Validating designs against Labor Code and FEHA requirements and real‑world conditions.
- Creating an AI audit trail: documenting system design, testing, updates, and remediation efforts.
- Maintaining human oversight: establishing review protocols for anomalies, complaints, or adverse outcomes.
- Managing vendor risk: incorporating compliance warranties, indemnification, and audit rights into vendor agreements.
- Leveraging PAGA cure and remediation mechanisms: promptly correcting errors and documenting remedial actions to reduce penalty exposure.
Conclusion
AI may streamline HR operations, but in California, it also amplifies risk. PAGA’s representative enforcement model transforms algorithmic mistakes into systemic, high-value claims. Employers that proactively audit, validate, and document their AI systems will be far better positioned to mitigate exposure as this next wave of employment litigation develops.
Kartikey A. Pradhan is a partner and Emma Adams is an associate within the labor and employment law group of Kaufman Dolowich’s San Francisco office.
Reprinted with permission from the May 15, 2026 edition of “The Recorder” © 2026 ALM Global Properties, LLC. All rights reserved. Further duplication without permission is prohibited, contact 877-256-2472 or reprints@alm.com. “
