Attention is not Explanation

Key idea

An attribution surface can be useful without being explanatory, and confusing the two leads to false confidence.

Lasting impact

It deepened my skepticism toward seductive interface outputs that look interpretable before they have earned that trust.

When I return to this

I come back to it whenever an AI system presents a neat confidence story or interpretability view that feels cleaner than the underlying evidence.

Why this source stayed with me

This paper stayed with me because it attacks a very common failure mode in technical work: we mistake something readable for something explanatory. That is not just a model-interpretability problem. It is a broader systems problem, and one that shows up anywhere dashboards, rationales, or visual traces create a false sense of understanding.

I appreciate sources like this because they force discipline. They do not reject useful tooling, but they ask you to be honest about what kind of claim the tool actually supports. That distinction still matters a lot in AI product work.

What I kept returning to

• The difference between interpretability as inspection and interpretability as explanation.
• The warning against treating attention visualizations as settled evidence.
• The broader lesson that interface legibility can outrun epistemic legitimacy.

Where it still shows up

It shows up whenever I design review surfaces for AI outputs. I want explanations, traces, and summaries to help people reason better, but I do not want the interface to pretend it has solved questions it has only rearranged. This paper keeps that line visible.

How I would hand it to someone else

I would hand this to someone building AI products, evaluation tooling, or interpretability features. It is especially useful for teams that are tempted to treat a readable artifact as proof instead of as one piece of evidence.