Introduction: Explainability Was Never the End Goal

For years, explainability has been treated as the ethical finish line for artificial intelligence.

If we could explain how models work—

If we could open the black box—

If we could point to features and weights—

Then accountability, we were told, would follow.

It hasn’t.

Explainability was a necessary phase, but it was never sufficient. As AI systems move from recommendation to decision-making—from analytics to authority—the core question is no longer how the model works, but who is responsible when it causes harm.

That shift marks the next phase of AI accountability: liability.

Why Explainability Falls Short in Practice

Explainability focuses on understanding.

Accountability focuses on consequence.

The two are not the same.

In real organizational settings:

• Explanations are technical, not actionable

• Decision-makers rarely engage with model logic

• Oversight is delegated rather than exercised

Research in governance and organizational behavior shows that transparency alone does not prevent harm when authority and incentives remain misaligned (Davenport & Miller, 2022). Leaders may understand how a system works and still abdicate responsibility for what it does.

Explainability answers “why.”

Liability answers “who pays.”

AI Has Crossed the Line From Advice to Authority

AI systems increasingly:

• Approve or deny credit

• Flag fraud and compliance risk

• Rank candidates and employees

• Allocate resources

• Trigger investigations or interventions

These are not advisory roles. They are decisional functions.

The Stanford AI Index documents that AI systems are now routinely embedded in high-stakes domains where errors carry legal, financial, and human consequences (Stanford HAI, 2024). Yet governance structures often treat these systems as tools rather than actors with delegated authority.

When AI decisions affect rights, livelihoods, or access to capital, accountability cannot remain abstract.

Liability Will Follow Capital, Not Code

Historically, regulation does not chase technology—it follows risk concentration.

Banking regulation did not emerge because spreadsheets existed.

It emerged because financial losses cascaded.

The same pattern is forming around AI.

McKinsey Global Institute notes that AI risk increasingly manifests as balance-sheet exposure, litigation risk, and reputational damage—not technical failure alone (McKinsey Global Institute, 2023). As AI-driven decisions impact revenue, compliance, and valuation, liability will land where capital sits: with executives, boards, and firms.

No amount of explainability documentation will substitute for accountable ownership when losses materialize.

The Legal and Ethical Gap Leaders Are Underestimating

Most organizations assume that if:

• A model is explainable

• A policy exists

• A committee reviewed it

Then risk is managed.

This assumption is fragile.

The OECD has made clear that accountability for AI outcomes must remain human and enforceable, regardless of system complexity (OECD, 2019). Courts, regulators, and insurers will not accept “the model did it” as a defense.

Explainability may inform investigations.

It will not absorb liability.

Why Boards and CFOs Will Be Pulled In

AI accountability is shifting upstream.

Boards and financial leaders will increasingly be asked:

• Who approved this system?

• What controls existed?

• Who had override authority?

• Why was deployment allowed given known risks?

MIT Sloan Management Review emphasizes that AI governance becomes effective only when embedded into enterprise risk management, not siloed within technical teams (MIT Sloan Management Review, 2023).

This is no longer an ethics discussion. It is a fiduciary one.

What the Next Phase of AI Accountability Requires

Moving from explainability to liability requires structural change.

At minimum:

• Named accountability: Executives explicitly responsible for AI outcomes

• Decision traceability: Clear records linking AI outputs to human approvals

• Override authority: Enforced human control over high-risk decisions

• Risk integration: AI treated as enterprise risk, not innovation theater

Explainability supports these measures—but it cannot replace them.

Accountability begins where explanations end.

The Reckoning Ahead

As AI systems become inseparable from business decisions, accountability will no longer be satisfied by transparency alone.

The question leaders must confront is not:

Can we explain the model?

But:

Are we prepared to own its consequences?

Explainability was the opening chapter.

Liability is the one being written now.

And organizations that fail to prepare will learn about accountability the hardest way possible.

If you are a leader responsible for AI-driven decisions—and want accountability frameworks that survive legal, financial, and reputational scrutiny—

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References

• Davenport, T. H., & Miller, S. M. (2022). When algorithms decide. Harvard Business Review, 100(5), 88–96.

• McKinsey Global Institute. (2023). The economic potential of generative AI: The next productivity frontier. McKinsey & Company.

• MIT Sloan Management Review. (2023). Governing AI responsibly: Practical frameworks for organizations. MIT Sloan Management Review.

• Organisation for Economic Co-operation and Development. (2019). Artificial intelligence and accountability: Who is responsible when AI goes wrong? OECD Publishing. https://doi.org/10.1787/5e5c1d6c-en

• Stanford Institute for Human-Centered Artificial Intelligence. (2024). AI index report 2024. Stanford University. https://aiindex.stanford.edu