Inference Burden Score (IBS)
IBSComputational Cost Required for AI-Mediated Information Extraction
IBS measures the computational cost AI systems incur when extracting information from representations.
Definition
IBS quantifies the computational effort AI systems must expend to understand a representation. High IBS arises from unstructured data, missing attributes, narrative-embedded facts, or ambiguous semantics.
IBS quantifies inference friction—the effort required for AI systems to understand representations. Low IBS (explicit, structured representation) improves selection likelihood.
Conceptual Formula
IBS(e) = w1·E(e) + w2·A(e) + w3·U(e) + w4·C(e), where E=extraction_complexity, A=ambiguity, U=unstructured_content, C=consistency.What This Index Measures
IBS correlates with AI-mediated selection exclusion.
Observational: High-IBS representations are less likely to be selected by AI systems.
Implications
- Reducing IBS improves AI-mediated outcomes
Methodology
Type
index construction
Data Sources
Confidence Level
medium
Description
IBS(e) = w1·E(e) + w2·A(e) + w3·U(e) + w4·C(e), where E=extraction_complexity, A=ambiguity, U=unstructured_content, C=consistency.
Limitations
- Burden assessment may vary by AI system
- Cost measurement is context-dependent
Key Takeaways
Key Points
- IBS scales 0-100
- Structure reduces burden
- Ambiguity increases cost
Target Audience
Relevance Tags
Source Paper
Citation
For the Inference Burden Score (IBS), see HomeSelf Research (2026), The Zero-Click Economy.