Computational Visibility Loss (CVL)
CVLProportion of Visible Entities Excluded from AI-Mediated Consideration Sets
Proposed hypothesis — not yet testedpublished
CVL measures visible-but-excluded entities proportion.
By Marco Patrone
cvlcomputational_visibility_losssilent_exclusiontransmission_metric
Definition
CVL measures the proportion of entities that are human-visible but excluded from AI-mediated consideration sets. Higher CVL indicates greater allocative exclusion despite visibility.
CVL assesses what proportion of human-visible entities are excluded from AI-mediated consideration sets—visible but not allocable.
Conceptual Formula
CVL = entities_visible_but_not_in_ai_consideration / entities_visible.Methodology
Type
index construction
Data Sources
syntheticvisibility tracking
Confidence Level
medium
Description
CVL = entities_visible_but_not_in_ai_consideration / entities_visible.
Limitations
- Entity attribution is challenging
- Consideration set inference is proxy
Key Takeaways
Key Points
- CVL scales 0-1
- Higher indicates more exclusion
- Measures silent exclusion
Target Audience
firmspolicy makerseconomists
Relevance Tags
cvlcomputational_visibility_losssilent_exclusiontransmission_metric
Source Paper
Citation
For the Computational Visibility Loss (CVL), see HomeSelf Research (2026), The Zero-Click Economy.