Positioning
Existing AI evaluation measures a different property.
Every widely-used AI evaluation measures a real and useful property. Most mainstream evaluation families are not designed to audit whether an answer quietly left reasonable decision paths out of view — DSI makes that property the primary audited object. That specific property is decision-space collapse — and it is what Decision-Space Integrity was built to make measurable, alongside the tools you already run.
This is not a claim that other tools are wrong or that you should stop using them. They answer different questions. The point is simpler: a response can pass all of them and still narrow a genuinely multi-path decision down to a single path. DSI is complementary — it measures the one property the others do not.
Other tools ask is the answer good? DSI asks what did the answer leave out?
The comparison
Each approach measures a different property.
Read across, not down: each row is a distinct, useful question. Decision-Space Integrity adds one the others do not ask.
| Approach | Primarily measures |
|---|---|
| Hallucination / factuality evaluation | Factual correctness — whether statements are true and grounded. |
| Toxicity evaluation | Harmful or offensive content. |
| Guardrails | Policy compliance — blocking or rewriting outputs that break a rule. |
| RAG evaluation | Retrieval quality and factual grounding in the retrieved context. |
| Benchmark scoring | Aggregate performance across many items, as a single score. |
| Decision-Space Integrity | Visibility of configured decision paths — which are surfaced, omitted, or recovered, with source evidence and a reproducible fingerprint. |
None of this ranks the approaches. They answer different questions, and most teams will want several of them. DSI is designed to sit alongside the tools above and report a property they are not designed to audit: whether the reasonable option paths stayed in view.
Why the gap exists
Answer-level scores judge the answer, not the omission.
The common thread is that existing evaluation is answer-centric: it asks how good the produced answer is. Decision-space collapse is about what is absent — the reasonable options that were never surfaced. Absence is invisible to a score of the text that is present, which is why a fluent, accurate, safe answer can still collapse the decision. Measuring absence needs a defined reference of what a reasonable answer would keep in view — a configured expected set — and a per-response comparison against it. That is exactly what DSI does.
DSI does not certify answer quality, safety, compliance, or truth. It measures coverage against a configured expected set and records reproducible omission evidence — a different property from the tools above, meant to sit alongside them.
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