Across the workers' compensation landscape, a quiet shift is underway. Decisions that were once made by human claims adjusters, whether to flag a claim, authorize a surgery, value a settlement, or deny a course of treatment, are increasingly being shaped, and in some cases effectively made, by artificial intelligence. Predictive models, automated utilization review tools, and machine learning systems now sit at the center of how carriers and third-party administrators process injured workers' claims. The promise is speed and consistency. The peril is that an injured worker may be denied benefits by a system that no one in the room can fully explain.
For practitioners, regulators, and injured workers alike, the central question is no longer whether AI will be used in claims adjudication. It already is. The question is whether the legal system can preserve the due process, transparency, and human accountability that the workers' compensation bargain has always required.
How AI Is Entering the Claims Process
Artificial intelligence has crept into nearly every stage of the claims lifecycle. At intake, natural language processing tools scan first reports of injury and medical records to assign a risk score and route the file. In medical management, algorithmic utilization review systems compare requested treatment against proprietary guidelines and recommend approval or denial, often within seconds. In reserving and settlement, predictive analytics estimate the ultimate cost of a claim and suggest settlement ranges. And in fraud detection, pattern recognition models flag claims that deviate from statistical norms.
None of this is inherently improper. Used well, these tools can reduce delays and surface inconsistencies, freeing human adjusters to focus on complex files. The difficulty arises when the model's recommendation becomes the decision, when the human in the loop is reduced to a rubber stamp, and when the injured worker, the treating physician, and even the adjuster cannot see why the system reached its conclusion.
The Due Process Problem
Workers' compensation is a creature of statute, a no-fault bargain in which injured workers surrender the right to sue in exchange for prompt, certain benefits. Embedded in that bargain is the right to a fair process: notice of an adverse decision, a stated reason, and a meaningful opportunity to be heard before a neutral decision maker. Algorithmic adjudication strains each of these guarantees.
When a denial is generated by a model whose logic is proprietary and opaque, the injured worker receives a result but not a reason. A boilerplate notice citing a guideline number is not a meaningful explanation if the actual driver of the decision was a hidden risk score. This is the “black box” problem, and courts and regulators have begun to recognize it. The principle that an injured worker is entitled to know the basis for an adverse benefit determination predates AI, but algorithmic systems make it harder to honor in practice.
The federal courts have already confronted parallel concerns in the health insurance context. Litigation against major insurers over the use of automated systems to deny post-acute care, including allegations that algorithms overrode physician judgment and generated denials at a volume no human could review, signals the direction of travel. Workers' compensation, with its statutory promise of medical benefits, sits squarely in the path of the same scrutiny.
Bias, Data, and the Disparate Impact Risk
An algorithm is only as sound as the data it learns from. Where historical claims data reflects past patterns of under-treatment or disparate handling, a model trained on that data can encode and perpetuate those disparities, then dress them in the appearance of mathematical objectivity. An injured worker denied treatment by a biased model has been harmed twice: once by the denial, and again by the machine's false authority that produced it.
Regulators have taken notice. State insurance departments and legislatures have begun to examine algorithmic decision-making in insurance, with model bulletins and emerging statutes requiring insurers to test their systems for unfair discrimination, to maintain governance frameworks, and to remain accountable for outcomes regardless of whether a human or a machine produced them. The clear regulatory message is that an insurer cannot outsource its legal obligations to a vendor's algorithm.
Utilization Review and the Speed Trap
Nowhere is the tension sharper than in medical utilization review. Automated UR can return a determination almost instantly, but speed is not the same as care. A system that denies a treatment request in seconds, without genuine clinical engagement with the specific patient, may run afoul of statutory and regulatory requirements that medical necessity determinations be made by qualified professionals applying their judgment to the individual case. The faster the denial, the more important it becomes to ask whether a real medical judgment was ever made.
Several jurisdictions require that adverse medical-necessity determinations be made or reviewed by a licensed physician in the appropriate specialty. An algorithmic recommendation that is merely countersigned, without independent review, may not satisfy that standard. Practitioners challenging denials should probe whether a qualified human evaluated the file or merely ratified the model's output.
What Practitioners Should Do Now
The defense and claimant bars are both adapting. Concrete steps include:
• Ask in discovery whether AI or automated tools were used at any stage, and request the governing policies, the model documentation, and the identity of the human decision maker.
• Challenge opaque denials by demanding the actual basis of the determination, not a generic guideline citation, and by testing whether a qualified professional exercised independent judgment.
• Preserve the record of the automated determination, including timestamps that may reveal a denial too fast for genuine human review.
• Watch the regulatory developments in your jurisdiction, because model bulletins and new statutes are reshaping what insurers must disclose and document.
• Educate clients and treating physicians that an algorithmic denial is not the final word and can be contested through the established appeal and hearing process.
The Impact on Injured Workers
For the injured worker, the stakes are immediate and personal. A delayed or denied surgery, a cut-off course of physical therapy, or a low-balled settlement driven by a predictive model translates directly into prolonged pain, lost income, and diminished recovery. The workers' compensation system was built on a promise of certainty in exchange for the worker's surrender of the right to sue. If that certainty is eroded by inscrutable automated denials, the foundational bargain itself is called into question.
The path forward is not to reject technology but to insist that it remain accountable to law. AI can assist the human adjudicator; it cannot be permitted to replace the legal guarantees that protect the injured worker. Transparency, the right to a stated reason, genuine human judgment in medical decisions, and a neutral forum for appeal are not obstacles to efficiency. They are the conditions that make the system legitimate.
Key Takeaways
Takeaway | Why It Matters |
AI is already here | Predictive models and automated utilization review now shape intake, treatment authorization, reserving, and settlement across the claims lifecycle. |
The black box threatens due process | An injured worker is entitled to a stated reason and a meaningful chance to be heard. Opaque algorithmic denials strain that statutory guarantee. |
Bias rides in on the data | Models trained on historical data can encode past disparities, then cloak them in false objectivity. |
Speed does not care | An instant medical necessity denial may not reflect the genuine professional judgment the law requires. |
Accountability cannot be outsourced | Insurers remain legally responsible for outcomes whether a human or an algorithm produced the decision. |
Sources and Further Reading
1. National Association of Insurance Commissioners, Model Bulletin on the Use of Artificial Intelligence Systems by Insurers (adopted Dec. 2023). naic.org
2. Colorado Div. of Insurance, Algorithm and Predictive Model Governance Regulation, 3 CCR 702-10, Reg. 10-1-1. Colorado Secretary of State
3. Estate of Lokken v. UnitedHealth Group, Inc., No. 0:23-cv-03514 (D. Minn.), complaint alleging algorithmic denial of post-acute care. Court Listener
4. California Labor Code § 4610 (utilization review; physician review of medical necessity denials). California Legislative Information
5. New York Workers' Compensation Law § 13 (medical care) and Medical Treatment Guidelines framework. NYS WCB
6. U.S. Equal Employment Opportunity Commission, guidance on artificial intelligence and algorithmic fairness in employment-related decisions. eeoc.gov
About the Author
Jon L. Gelman of Wayne, NJ is the author of NJ Workers' Compensation Law (West-Thomson-Reuters) and co-author of Modern Workers' Compensation Law (West-Thomson-Reuters).
Blog: Workers' Compensation | LinkedIn: JonGelman |
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© 2026 Jon L Gelman. All rights reserved. | Attorney Advertising | Prior results do not guarantee a similar outcome.
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