Explainable AI in Workers’ Compensation:
Explainable AI in Workers’ Compensation:
A Practical, Ethical Guide for Claims, Legal, and Medical Professionals
The MedLegal Professor’s Technology Lab™ Series
By Nikki Mehrpoo – The MedLegal Professor™
Featured on WorkersCompensation.com | Powered by MedLegalProfessor.ai
Artificial intelligence is rapidly becoming a cornerstone of the workers’ compensation system.
In this new reality, I find that even the most seasoned professionals are asking the same foundational questions. My goal today is to move you from overwhelm to empowerment by answering these questions with clarity, practical examples, and real-world relevance for your high-stakes work.
From Overwhelm to Empowerment: Making Sense of AI in Workers’ Compensation
Artificial intelligence (AI) is rapidly becoming a cornerstone of the workers’ compensation system. Yet many professionals—including claims adjusters, attorneys, nurse case managers, risk managers, and employers—still ask:
What does AI really do in workers’ comp?
Will it help me or eventually replace me?
How can I ensure the tech is fair, legal, and transparent?
These questions are not signs of resistance. They are signs of leadership. The truth is: You do not need to become an engineer. You need to become an ethical, informed user.
👉 Start here: Ask your vendor or IT lead, “What exactly does this AI tool do, and can I see a sample of its decision-making?”
This article breaks it all down with clarity, practical examples, and real-world legal-medical relevance.
To answer these questions, we must start with the most important concept in ethical automation.
What Is Explainable AI and Why Does It Matter in Workers’ Comp?
Explainable AI (XAI) is a form of artificial intelligence that allows humans to understand, audit, and justify the logic behind an automated decision. It shows its work, just like we teach professionals to do in court, clinical review, and compliance audits.
AI Tools That Work for You, Not Around You
Non-explainable AI: “Flag this claim.” No reasons. No trace.
Explainable AI: “Flagged for X due to pattern Y in Document Z” with a full logic trail.
Benefits of Explainable AI:
Verifiable reasoning
Audit-readiness
Legal defensibility
Early error detection
Ethical alignment
🛠️ Practical Tip: Look for tools with a “click-and-trace” feature. You should be able to click on any AI-generated flag or recommendation and see exactly what triggered it.
“Invisible AI makes visible mistakes.”
This isn't a future problem; the need for explainability is already here.
How AI Is Already Shaping Claims and Compliance Workflows
AI is already being used to:
Prioritize incoming claims
Flag potential fraud
Evaluate medical necessity
Auto-deny or authorize treatment
Recommend settlements
These systems are influencing everything from litigation risk to patient outcomes. That is why understanding them is not optional.
“When you cannot explain AI, it is not just a tech issue. It is a liability.”
🛠️ Practical Tip: For every AI recommendation, ask: “Can I explain this decision to a judge, regulator, or injured worker?” If not, the system may be putting your license or your case at risk.
This new reality demands a new framework for control, one that empowers professionals instead of replacing them.
The AI + HI™ Model: Combining Automation with Human Oversight
At MedLegalProfessor.AI™, we use the AI + HI™ framework, where AI means automation and HI means human intelligence—judgment, ethics, and accountability.
“You don’t need to code it. You need to control it.”
AI + HI™ = Faster results + Ethical review + Trustworthy output
🛠️ Practical Tip: Assign one “AI reviewer” per team. Their job is to check if outputs align with legal, medical, and operational standards. No AI tool should be left unsupervised.
This model is critical because when AI makes decisions that impact rights and benefits, it crosses a crucial line.
Why Explainability Is Now a Compliance Requirement
When AI is used to:
Deny treatment
Refer a claim to Special Investigations
Auto-settle indemnity exposure
Flag a doctor for overutilization
…it is not just automation. It is adjudication.
Leaders and their teams across the industry must understand and be able to explain the AI’s involvement. Whether you manage a claims desk or legal practice, you need to know how the system reached its conclusion and what role humans played.
🛠️ Practical Tip: Build AI literacy into your compliance strategy. Ask during training: “Who can explain this decision? Who signed off? Is it traceable?” That question builds a culture of ethical accountability.
To build this culture, here are the five questions I insist every team uses as their guide.
Five Questions Every Claims Leader Must Ask Before Using AI Tools
Before you trust a claims triage tool, automation engine, or predictive model, ask:
Can I clearly explain the decision to a peer, claimant, or regulator?
Can I see the underlying documents or data it used?
Can I correct or override its output if needed?
Does the vendor allow human review of all outputs?
Is there an audit trail that documents the full process?
If the answer to any is “no,” you are operating without a net.
🛠️ Practical Tip: Post these five questions at every review desk. Use them as a readiness test before onboarding any system.
Knowing the right questions also helps you spot the red flags in tools that create unacceptable risks.
Common Red Flags: When AI Tools Create Legal and Ethical Risk
Avoid tools that:
Use mystery “scores” with no justification
Make irreversible decisions
Offer “just trust us” logic
Lack downloadable logs or reports
No human judgment? No human justice.
🛠️ Practical Tip: In every vendor demo, ask: “Can you walk me through how I would audit or override this system’s decision?” If they hesitate, you should too.
The value of this vigilance is not theoretical; it has already saved organizations from significant liability.
Case Study: Avoiding a Lawsuit With Explainable AI
A national TPA flagged a “high-risk” claim using AI. A senior reviewer noticed that the tool had factored in ZIP code and age—potentially discriminatory inputs. Because the system was explainable, the logic trail was visible. They corrected the recommendation, documented the oversight, and avoided liability.
Result: No lawsuit. No bad press. Just responsible tech and strong human review.
🛠️ Practical Tip: Run random monthly audits of AI-flagged claims. This helps detect hidden bias and ensures your system holds up under legal review.
To protect your own operations, here is a simple checklist to get you started.
Start Here: Simple Ways to Make Your Team AI-Ready Today
Designate an AI ethics reviewer per department
Include “AI explainability” in every vendor contract
Train teams on your five-question audit
Create escalation protocols for AI-generated errors
Use human-in-the-loop workflows in every automation process
🛠️ Practical Tip: Empower every team member—legal, claims, or clinical—to say: “I do not trust that AI output. Let us review it together.” That one statement can protect lives, licenses, and livelihoods.
My Final Insight
This is not about tools. This is about trust. AI can help you get started and move faster, but only you, the licensed professional, can make it right, legal, and worth trusting. These tools will not replace you. They will elevate you, but only if they are built for ethics, not just efficiency. We do not fix broken systems with shinier software. We fix them with smarter professionals, stronger oversight, and explainable AI.
📢 Take the Next Step: Empower Your Team with Ethical AI
Still using tools you cannot explain? Time to upgrade.
Visit MedLegalProfessor.AI to explore tools, downloads, and AI compliance strategy.
Contact The MedLegal Professor at [email protected]
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