Is Your AI Ready to Stand Trial?

How to Build Legal Defensibility into Every AI System You Use

By Nikki Mehrpoo, JD, AIWC, AIMWC

Founder, The MedLegal Professor™ | AI Legal Governance Strategist | Former Workers’ Compensation Judge

If your AI can’t stand up in court, it isn’t ready for the real world.

That’s not a soundbite. It’s the standard I hold every system to. Whether you’re in healthcare, legal services, insurance, or a regulated SaaS space, AI must meet more than performance goals—it must meet compliance thresholds. AI tools are fast, smart, and scalable, but I have to ask: are they legally defensible?

Here’s what that really means and how you can ensure your tools don’t just work but hold up under the pressure of professional scrutiny.

To help you determine if your own systems meet this standard, I've developed a comprehensive checklist. Use this before deploying any AI tool that influences client outcomes, legal filings, patient records, claim decisions, or compliance processes.

The Legal Defensibility AI Checklist

✅ Explainability: Can the AI’s output be explained clearly in nontechnical terms? Could a judge, opposing counsel, regulator, or client understand how the result was generated?

✅ Transparency: Are the data sources, training methodology, model version, and parameters known? Can you show the inputs and outputs for each task?

✅ Reliability: Is the tool consistent across use cases? Are its known error rates documented and acceptable for regulated decisions?

✅ Functional Appropriateness: Was the AI built for this use? Is it a suitable tool for the outcome it is influencing (e.g., patient treatment plan, legal brief, or claim denial)?

✅ Human Oversight: Is a licensed and qualified professional responsible for reviewing the AI’s work before it affects stakeholders?

✅ Regulatory Compliance: Does the AI comply with data protection laws (HIPAA, GDPR, CPRA), anti-discrimination statutes, and intellectual property rights?

✅ Bias and Risk Mitigation: Is the AI regularly audited for bias, hallucinations, or unsafe outputs? Are you actively mitigating these issues?

✅ Documentation: Do you have prompt logs, model versions, vendor details, and data lineage records on file?

✅ Output Verification: Are outputs reviewed for factual, legal, or clinical accuracy? Especially citations, metrics, and case references?

✅ Testimony-Ready: Can an expert witness explain your system in court, with clarity and confidence?

This checklist isn't just about technical compliance; it's a fundamental business strategy.

Why Legal Defensibility Is a Business Strategy

AI that can’t be explained can’t be defended. And AI that can’t be defended exposes your business to:

• Regulatory sanctions

• Malpractice liability

• Contractual breach claims

• Compliance failures

• Lost investor confidence

In litigation-heavy industries, defensibility is the strategy.

For those just starting out, the good news is you don't need to be an engineer to build a defensible process.

For Hesitant and Novice Users: What to Do Right Now

You don’t need to understand the algorithm. You do need to control how it’s used. Here’s how:

• Ask vendors hard questions. What is the AI trained on? How is it validated? What’s the governance protocol?

• Keep a usage log. Record prompts, model versions, and any edits made to the AI’s output.

• Train your team. Legal, clinical, and claims professionals should know how to spot hallucinations, bias, and misuse.

• Set human oversight rules. Nothing should go out without a licensed review.

• Create documentation templates. For every AI task, define how and why it’s used—and how the results are verified.

Let’s look at how this plays out in a real-world scenario.

Sample Use Case: AI in Healthcare Litigation

Scenario: An AI-generated summary of a patient’s record is submitted in a workers’ compensation case.

Risks: Inaccuracy, data omission, misclassification of injuries.

Defensibility Test: Was the summary reviewed by a licensed clinician? Was the AI source documented? Can it be re-generated for the court?

If not, that output may not survive cross-examination.

My Final Word

I build AI systems for the real world—systems that stand up in audits, in boardrooms, and in court.

If your AI fails the defensibility test, you don’t just risk inefficiency. You risk lawsuits, lost trust, and leadership credibility. Legal defensibility is not just compliance. It is infrastructure for responsible innovation.

So I ask you to look at your AI and consider: If this gets challenged, can I stand by it?

If not, let’s fix that. Because in regulated industries, AI that cannot be defended cannot be deployed.

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