Insight · Yalla2x
Explainability is not evidence
Two different jobs
Explainability answers "why did the model do that?" with a human-readable account — feature importances, attention, a rationale. Evidence answers "what exactly happened, on what data, and has it been changed?" with a record you can check. They are often confused because both feel like transparency.
This is adjacent to our argument that proof beats a probability: a story about a decision, like a score about a decision, is still something you have to trust.
Why an explanation can mislead
A generated explanation is itself a model output. It can be fluent, reasonable, and wrong about the real cause — a post-hoc rationalisation rather than a faithful account. In low stakes that is fine. In a dispute, a plausible-but-unverifiable explanation is worse than nothing, because it invites false confidence.
The courtroom test is unforgiving: an explanation does not survive cross-examination; a tamper-evident record of the actual decision does.
Use both, for their own jobs
The point is not to abandon explainability. It is to stop asking it to do evidence's job:
- Use explanations to understand, improve and debug models.
- Use sealed, replayable records to prove what was decided and defend it.
- Never let an explanation stand in for provenance where accountability is at stake.
Evidence first where it counts
For the decisions that carry real risk, build on evidence and add explanation on top — not the other way round. Yalla2x seals material decisions so they can be replayed and independently verified; the explanation is a convenience, the record is the foundation. More on our approach, or bring us a decision you would need to defend.
Frequently asked
What is the difference between explainable AI and verifiable AI?
Explainability produces a human-readable account of why a model decided something, generated after the fact. Verifiability produces a sealed, replayable record of what was actually decided, on what data, unaltered since. One aids understanding; the other provides evidence.
Can an AI explanation be wrong?
Yes. An explanation is itself a model output and can be fluent yet unfaithful to the real cause — a post-hoc rationalisation. That is why explanations should not stand in for provenance where accountability matters.
Should regulated industries rely on explainability?
They should use it to understand and improve models, but rely on tamper-evident records for proof. In a dispute, a plausible but unverifiable explanation does not hold up the way a replayable record of the actual decision does.