Explainability Isn’t Optional: Why Clarity Builds Credibility in AI Systems
Explainability Isn’t Optional:
Why Clarity Builds Credibility in AI Systems
By Nikki Mehrpoo, JD, AIWC, AIMWC
Founder, The MedLegal Professor™ | AI Legal Governance Strategist | Former Workers’ Compensation Judge
When AI systems are used in sensitive fields like law, medicine, insurance, and government, one question matters more than anything else: Can we explain how the AI made its decision?
If the answer is no, the AI is not ready. Not for court. Not for compliance. Not for professional use. I’ve seen firsthand from my time on the bench and as a strategist that in regulated industries, explainability is a legal and ethical necessity. If you cannot explain your system’s decision, you cannot justify its use when it matters most—under oath, under audit, or under public scrutiny.
But "explainability" is a term that gets thrown around a lot. In my work, I've found it boils down to three core, non-negotiable components.
What Does Explainability Actually Mean?
If you’re new to AI, here’s what you need to know:
Plain Language
Can the AI’s output be explained in terms everyone understands? That means a judge, jury, regulator, clinician, or client must be able to grasp how the answer was generated.Traceable Logic
Can you walk someone through the exact steps the AI took to reach its decision? The trail must be visible, like following a breadcrumb path from input to outcome.Known Error Rate
What kinds of mistakes does the system make? How often? Under what conditions? Knowing this is essential. You do not want to rely on results you cannot verify.
This isn't just a technical definition; it has profound real-world consequences for every licensed professional.
Why It Matters in Real Life
In legal disputes, licensing board reviews, and regulatory audits, unexplained AI = indefensible AI. If your system cannot withstand legal scrutiny, it could become a liability to your team, your reputation, and your bottom line.
You are still responsible for the AI’s output, even if you did not write the code. That means you must know what it is doing and why.
To help you assess your own systems, I've developed a practical checklist. It's designed for professionals who aren't AI engineers but are still accountable for the technology being used in their organizations.
The Practical Explainability Checklist
Can your team explain the AI’s output in plain English?
Do you know the source of the data used to train the system?
Can you trace the logic from input to result?
Are the AI’s error types and rates documented?
Is there human review of all outputs?
Can your compliance officer explain the system to a regulator or board?
Is there documentation for each decision the AI makes?
Would the AI’s results hold up under legal cross-examination?
If the answer to any of these is no, then the system needs more governance before it enters high-stakes use.
Building this kind of governance is a serious undertaking, but you don't have to start from scratch. I often point my clients to these publicly available frameworks from trusted institutions for expert direction.
Trusted External Resources for Explainability
Here are publicly available frameworks and guides that provide expert direction on building transparent and explainable AI systems:
NIST AI Risk Management Framework (RMF)
European Commission Ethics Guidelines for Trustworthy AI
ISO/IEC 23894:2023 Artificial Intelligence Risk Management Standard
Alan Turing Institute – Explainable AI Research Hub
Google Model Cards for Transparency
DARPA Explainable AI (XAI) Program
AI Incident Database (Documenting AI Failures and Risks)
Ultimately, the message is simple. In our professions, clarity is not just a feature; it is the foundation of credibility. If your systems are a "black box," you cannot build trust with clients, courts, or regulators. You must be able to stand behind every decision your technology assists with, and that begins and ends with explainability.
Partner with Nikki Mehrpoo to ensure your AI system is legally defensible, explainable, and ready for court or audit.
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