Attribution Confidence Interval

Unlike binary attribution (cited/not cited), confidence intervals express attribution as a probability distribution. The interval quantifies the range within which the true attribution likelihood falls, typically at a 95% confidence level. This accounts for the inherent ambiguity when an AI synthesizes information from multiple overlapping sources. For example, a claim might have an 85% probability of originating from Source A, with a confidence interval of ±7%, indicating the true probability lies between 78% and 92%.

The interval width directly reflects attribution ambiguity—the degree to which a claim could have been derived from multiple sources. Key contributing factors include:

• Semantic overlap between candidate source documents
• Paraphrastic regeneration where the AI rewrites source material
• Multi-source fusion where a single claim synthesizes several references
• Training data memorization versus real-time retrieval A narrow interval indicates high attribution certainty; a wide interval signals significant ambiguity requiring human review.

Confidence intervals must be empirically calibrated against known attribution cases to ensure statistical validity. This involves:

• Holdout testing on documents with known provenance
• Perturbation analysis where source text is deliberately modified to measure interval sensitivity
• Inter-annotator agreement studies comparing AI attribution to human expert judgments A well-calibrated interval means that 95% confidence intervals contain the true attribution value approximately 95% of the time. Miscalibrated intervals produce overconfident or underconfident attributions.

Attribution confidence naturally decays over time as source documents are updated, retracted, or superseded. Confidence intervals incorporate temporal decay functions that widen the interval as the time since citation increases. This reflects growing uncertainty about whether the cited source still supports the claim. For rapidly evolving fields like medical research or technology, decay rates are accelerated. For historical or mathematical claims, decay may be negligible.

When claims derive from multi-modal sources—such as text extracted from a chart in a PDF, or a statement transcribed from a video—confidence intervals must account for cross-modal extraction error. The interval widens to reflect:

• OCR inaccuracies in image-to-text conversion
• Speech-to-text transcription errors
• Visual-to-claim interpretation ambiguity Each modality introduces distinct error distributions that compound in the final attribution confidence calculation.

Confidence intervals enable automated decision logic for downstream systems:

• Above 90% lower bound: Auto-publish with full citation
• 70-90% lower bound: Publish with 'medium confidence' flag
• 50-70% lower bound: Queue for human editorial review
• Below 50% lower bound: Suppress citation entirely or mark as unverified These thresholds transform statistical outputs into operational workflows, ensuring that only high-confidence attributions reach end-users without manual intervention.

A quantitative metric evaluating the trustworthiness of a cited source based on factors like author expertise, domain authority, and historical accuracy. This score serves as a prior probability input when calculating the final Attribution Confidence Interval.

• Combines signals: author H-index, domain age, retraction history
• Acts as a Bayesian prior for confidence calculations
• Directly influences the lower bound of the confidence interval

A composite metric that quantifies the degree of semantic and factual correspondence between a specific AI-generated statement and the content of its cited source. This score is the primary determinant of the confidence interval's width.

• Uses natural language inference (NLI) to detect entailment vs. contradiction
• High alignment narrows the confidence interval
• Low alignment triggers source ambiguity flags

The process of identifying when a cited source's content has been updated or altered post-citation, potentially invalidating the original evidence. Drift detection directly widens the Attribution Confidence Interval to reflect increased uncertainty.

• Monitors reference provenance hashes for tampering
• Flags time-decayed citations where content has diverged
• Triggers re-verification workflows when drift exceeds threshold

A verification technique that checks for agreement among multiple independent, high-quality sources to confirm a claim. High consensus narrows the confidence interval by reducing single-source dependency risk.

• Requires at least 3 independent corroborating sources for high confidence
• Uses co-citation analysis to identify reinforcing evidence clusters
• Penalizes echo-chamber citations from the same origin domain

A predictive score estimating the likelihood that a generated statement is a hallucination, calculated by analyzing the absence of supporting citations and internal model uncertainty signals. This index inversely correlates with the Attribution Confidence Interval.

• High risk index forces wider confidence intervals
• Incorporates model logit entropy and attention pattern analysis
• Used to gate whether a claim is surfaced to users at all

A classification of how precisely a citation points to its evidence, ranging from a full document to a specific passage, sentence, or data point. Finer granularity yields tighter confidence intervals by reducing the search space for verification.

• Levels: Document → Section → Paragraph → Sentence → Span
• Span-level attribution provides the narrowest confidence interval
• Coarse granularity introduces ambiguity penalties