The Explainability Mandate: Why Transparency Is the Next Compliance Frontier

By Meredith Anastasio, Managing Director Emerging Technology, Opal Group As artificial intelligence embeds itself deeper into decision-making systems, from credit approvals and hiring algorithms to fraud detection and audit automation, one principle keeps surfacing in regulatory, ethical, and boardroom conversations alike: explainability.

In a world where black-box models make high-stakes decisions, explainability is no longer a luxury. It is a compliance imperative.

When Compliance Cannot Explain the “Why”

AI models often rely on layers of abstraction too complex for even their creators to fully unpack. This makes it difficult, if not impossible, for compliance leaders to answer basic questions:

• Why was this decision made?

• Was the process fair?

• Can we defend this if challenged by a regulator, auditor, or stakeholder?

In traditional systems, audit trails provide clarity. In AI, those trails are often murky, probabilistic, or nonexistent. And that presents a serious risk.

The Regulatory Drumbeat for Transparency

Across jurisdictions, regulators are increasingly prioritizing transparency and interpretability. The EU AI Act, the FTC’s guidance on algorithmic accountability, and emerging frameworks from NIST and ISO all point in one direction: systems must be explainable to the people they affect and to those who govern them.

This isn’t just about fairness or user trust. It’s about legal defensibility. If your AI system cannot provide a rationale for its decisions, then neither can your compliance team. And that is a problem.

The Cost of Ignorance

When explainability is lacking, risk proliferates. Consider:

• Bias that hides in training data and outputs decisions with disparate impact.

• Automation surprises that lead to reputational damage when systems “learn” the wrong behaviors.

• Opaque failures that undermine internal investigations, external audits, or legal inquiries.

Without explainability, organizations are essentially flying blind, and so are regulators. That is why transparency isn’t just a technical feature; it’s a governance necessity.

Building a Culture of Explainable AI

So, what can compliance leaders do now?

1. Prioritize Model Auditability – Ensure all AI systems used in sensitive workflows can be reverse-engineered or are accompanied by explainable overlays.

2. Embed Compliance in the Design Phase – Partner early with data scientists to set explainability standards before systems go live.

3. Advocate for Human-Understandable Outputs – Push vendors and internal teams for solutions that are interpretable by non-technical users.

4. Train for AI Fluency – Equip your teams to ask the right questions: What features drive this model? How does it weigh different factors? Is the output understandable?

The Future Belongs to the Transparent

AI may be reshaping the future, but it must do so in a way we can explain, defend, and trust.

That’s where compliance steps in, not just to audit the outputs, but to demand clarity from the inside out.

Because in the end, if we can’t explain it, we can’t govern it.

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