The Confidence Gate Theorem: When Should Ranked Decision Systems Abstain?
Ranked decision systems -- recommenders, ad auctions, clinical triage queues -- must decide when to intervene in ranked outputs and when to abstain. We study when confidence-based abstention monotonically improves decision quality, and when it fails. The formal conditions are simple: rank-alignment and no inversion zones. The substantive contribution is identifying why these conditions hold or fail: the distinction between structural uncertainty (missing data, e.g., cold-start) and contextual un
Record details
Published: 10 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 14 July 2026
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ethics.ai (10 March 2026), “The Confidence Gate Theorem: When Should Ranked Decision Systems Abstain?,” evidence record 7414, https://ethics.ai/record/7414 (originally published by arXiv).
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