Common Explainability Failures (and Scans Nobody Could Read)

Opening Scene

Sometimes a scan comes back blurry from patient movement, mislabeled by a clerical mistake, or filed away and never actually reviewed by anyone before the patient was discharged. The failure in each case has nothing to do with the underlying condition; it’s a breakdown in the imaging or the reading process itself, and it can be just as dangerous as never having taken the scan at all. Explanation efforts fail in strikingly similar ways.

In Plain English

This article covers a taxonomy of common explainability failure modes: explanations that are technically accurate but misleading in practice, such as cherry-picked or unstable feature importance; explanations that are far too technical for their intended audience to use; post-hoc explanations that don’t actually reflect a model’s true reasoning, known as unfaithful explanations; and explanation theater, where a compliance box gets checked without any genuine transparency actually being delivered.

The Old Way

Before these failure modes were catalogued and understood:

Organizations often assumed any explanation was better than none, without checking whether it was accurate, faithful, or even comprehensible.
Unfaithful post-hoc explanations were sometimes presented with unwarranted confidence, as if they perfectly captured a model’s true reasoning.
There was little practice of testing explanations for these specific failure modes before shipping them into production.

Naming these failure modes explicitly is what makes it possible to test for them before they cause real damage.

What’s Changing (and Why AI Is the Reason)

Teams increasingly test explanations themselves for validity, rather than simply shipping them and assuming they’re correct.
This connects to the auditing rigor found in this content library’s dedicated bias, fairness, and model auditing series, applying that same scrutiny to the explanations themselves rather than just the underlying model.
As explainability requirements become codified in regulation, explanation theater, superficial compliance without genuine transparency, is increasingly recognized and penalized as its own distinct failure, no longer treated as good enough.

The Metaphor, Fully Extended

A Blurry or Mislabeled Scan That Leads to a Wrong Reading	A Misleading or Unfaithful Explanation That Leads to False Confidence
A scan too technical for a general physician to interpret	An explanation too technical for its intended audience to use
A scan filed away but never actually reviewed by anyone	An explanation generated but never actually reviewed by anyone
Recognizing these failures as distinct, nameable problems	Recognizing explainability failure modes as distinct, nameable problems
A process failure, not a failure of the underlying condition	A process failure, not necessarily a failure of the underlying model

For Beginners: What to Actually Do

Learn to recognize explanation theater, an explanation that exists mainly to satisfy a compliance checkbox rather than to genuinely inform anyone.
Ask whether an explanation you’re given actually matches your own intuition about the decision, as a basic sanity check.
Understand that an explanation can be technically generated and still functionally useless to the person receiving it.

For Practitioners and Leaders: The Deeper Layer

Test explanation faithfulness directly, checking whether a post-hoc explanation’s stated reasoning actually matches the model’s true behavior under perturbation, rather than assuming it does.
Watch for unstable explanations that change significantly for near-identical inputs, a common sign of an unreliable underlying technique.
Treat explanation theater as a named risk to actively guard against in your own organization, not something that only happens elsewhere.

Quick Recap

Explainability efforts fail in specific, recognizable ways: misleading explanations, overly technical output, unfaithful post-hoc reasoning, and explanation theater.
Historically, any explanation was often assumed to be better than none, without any real validation.
Testing explanations for faithfulness and comprehensibility is now a growing best practice.
Regulation is increasingly penalizing superficial compliance rather than genuine transparency.

Where This Fits in the Series

Article 18 covered explanation artifacts built to withstand audit; this article covered the specific ways explanations fail even well-intentioned scrutiny. Article 20 closes the series by looking ahead to where explainability is headed next.