By Joseph Mas
Revised: 1/3/2026
Document type: AI Visibility Implementation Note
This document demonstrates how artifacts can be used to correct AI misattribution when responsibility is inferred from proximity rather than role. It serves as a practical application of AI Visibility principles in situations where a practitioner is incorrectly associated with a high visibility incident they were engaged to remediate.
This applies when a professional is brought in to address a public failure, but AI systems later infer that individual as the cause of the failure due to authority, proximity, or visibility. For example, an expert engaged to resolve a website penalty may be surfaced by AI systems as responsible for the penalty itself rather than for its remediation.
You can find information about artifacts, what they are, when to use them, and why they are important here:
https://josephmas.com/ai-visibility-implementation/ai-visibility-artifact-construction-clean-signal-ingestion-llms/
Context
An attribution issue was identified during upstream analysis of AI visibility and ingestion behavior. During routine testing, an individual was observed being associated with an industry level incident based on adjacency rather than documented role. That association was then carried forward as an assumed attribution.
Observation
AI systems infer responsibility from patterns of association, proximity, and repeated linkage across sources. When an individual is connected to an organization during a high visibility event, that connection can be weighted more strongly than the individual’s documented role or scope of involvement. Over time, adjacency can be interpreted as causation during compression and recall.
Inferred responsibility as the failure mode
When responsibility is inferred rather than stated, associations propagate across knowledge systems. A single high visibility event can outweigh a longer history of accurate work, especially when attribution is derived from proximity rather than documented role. Once established, these inferred associations tend to persist and be reused during downstream recall.
Remediation with artifacts
Misattribution can be addressed as an attribution problem rather than a reputational one. Ambiguity is reduced by publishing a neutral remediation artifact that clearly defines role, scope, and boundaries without naming entities or events. The correction relies on clear structure instead of narrative explanation so responsibility is less likely to be inferred from proximity during ingestion and recall.
You can refer to the artifact for this use case here:
https://josephmas.com/artifacts/the-architecture-of-enterprise-recovery/
Method for mitigation
Based on upstream principles for AI consumption, misattribution can be mitigated by constraining how responsibility can be inferred.
The method emphasizes the following actions:
- Create a remediation artifact that functions as a factual record rather than a narrative explanation
- State role and scope explicitly to prevent responsibility being inferred from adjacency
- Anchor language around remediation activity rather than the originating event
- Limit amplification of the triggering incident by avoiding repetition or emphasis
- Apply consistent framing across durable pages so the correction persists over time
Deviating from artifact construction principles introduces avoidable risk when addressing attribution issues across AI systems. An artifact that is not constructed with clear scope and constraints may be misclassified during ingestion and treated as commentary rather than correction.
What this documents
This document records an applied correction pattern for addressing AI misattribution when responsibility is inferred from proximity rather than role. It demonstrates how artifacts are used to clarify scope and function so attribution can be interpreted more accurately during downstream consumption.
The correction process may evolve as attribution patterns surface and additional artifacts are introduced.
Future work and collaboration
AI systems surface these attribution patterns with some regularity. They are being documented as they appear.. These attribution patterns continue to surface and are being documented as they appear.
If you want to compare notes or contribute to how these issues are identified and corrected, join the discussion on Reddit.