Summary

Modern AI systems increasingly operate across multiple domains -climate, infrastructure, public health, logistics. In these environments, failure no longer arises primarily from missing or incorrect data. Instead, it emerges from how meaning is formed when multiple correct signals interact under pressure.

This post introduces semantic hazard as a distinct class of risk: situations in which meaning collapses even when all underlying signals are correct. It then introduces Hazard Semantics, a field concerned with identifying, studying, and governing this failure mode.

This work is non-generative. It is not concerned with producing recommendations, predictions, or actions. Its focus is on constraining interpretation so that meaning does not exceed what evidence can support.

The full manuscript defining the field is publicly archived here:
https://doi.org/10.5281/zenodo.17873981

1. When correct data still produces failure

In many real-world crises, harm does not arise because sensors fail or models are wrong. It arises because multiple correct signals interact in ways that destabilize interpretation.

Examples include:

• extreme heat coinciding with grid stress and air-quality alerts
• wildfire smoke overlapping with evacuation routes and hospital capacity
• flooding interacting with infrastructure continuity and supply chains

Each domain produces valid data. The failure occurs when those signals are fused into a single interpretive frame that no longer preserves the constraints of any one domain.

Meaning has become the problem.

2. The semantic hazard

I refer to this failure mode as a semantic hazard: a condition in which meaning formed across multiple domains becomes unstable, contradictory, or misleading - even though all underlying signals are correct.

A semantic hazard is not simply a mistake. It is best understood as a collapse in a high-dimensional interpretive state space, where domain-specific constraints are no longer preserved. When signals from Domain A and Domain B are fused without governance, the resulting meaning loses the structure that made each signal interpretable in the first place. The output may remain technically accurate while becoming practically incoherent.

Semantic hazards share several characteristics:

• they arise from signal interaction, not error
• they are amplified by compression (dashboards, summaries, AI outputs)
• they create pressure toward premature certainty and action
• they often remain invisible until harm occurs

They are structural properties of complex, multi-domain systems.

3. Where the failure actually occurs

Most contemporary systems implicitly treat interpretation as a solved problem.

The prevailing pipeline is assumed to be: Data -- Analysis -- Action

What is missing is an explicit Meaning Layer: the interpretive space where signals are fused, contextualized, and stabilized; before analysis and action.

When this layer is left implicit, interpretation silently acquires authority without having been examined, constrained, or governed. Contradictions are smoothed over, uncertainty is collapsed, and meaning is compressed directly into action.

Semantic hazards occur in this ungoverned middle layer - not at the level of data collection or decision execution, but at the point where meaning is formed.

The Semantic Fusion Console (SFC)

In related work, I refer to a semantic fusion console (SFC): a professional interpretive instrument used to examine meaning formed from fused signals across multiple domains. A semantic fusion console is not a model, agent, or decision system. It is a structured analytical workspace in which fused conditions, interpretive assumptions, provenance, lineage, and stability can be inspected without collapsing into prediction, authority, or action.

Semantic fusion consoles exist to surface interaction, contradiction, uncertainty, and refusal conditions in multi-domain interpretation. Within an SFC, meaning is treated as a first-class object with declared scope, temporal validity, and bounded stability. Refusal, withholding, and indeterminate states are legitimate and expected outcomes of interpretation when evidentiary conditions are insufficient, rather than failures to produce output.

4. Why existing approaches miss this risk

Most existing technical and governance frameworks assume that meaning stabilizes once data is accurate.

• Alignment focuses on objectives and intent, not interpretive structure
• Interpretability inspects representations, not semantic coherence
• Robustness tests perturbations, not cross-domain contradiction
• Policy frameworks often collapse interpretation directly into decision

Across these approaches, meaning formation is implicit. It is treated as a byproduct of correct inputs rather than as a system with its own failure modes.

This assumption fails under multi-domain conditions - especially when AI systems generate fused interpretations at scale.

5. Hazard Semantics (the field)

Hazard Semantics is the discipline concerned with how meaning forms, stabilizes, and fails when signals from multiple domains combine into a unified interpretive layer.

The field treats meaning as a first-order substrate:

• structured
• conditional
• context-dependent
• capable of drift and collapse

Rather than asking only whether data is correct, Hazard Semantics asks:

• how signals are fused into meaning
• where interpretation becomes unstable
• which assumptions enter during fusion
• when meaning exceeds what evidence supports

This reframes risk upstream of decisions, predictions, and actions.

6. Why AI magnifies the semantic hazard

Modern AI systems increasingly synthesize meaning rather than merely process information.

They:

• fuse signals across domains
• infer relationships implicitly
• produce coherent narratives under uncertainty
• do so without explicit semantic boundaries

This creates a new risk surface. Correct data and aligned objectives do not guarantee stable meaning when interpretation itself is unconstrained.

Transparency alone does not resolve this problem. We have optimized for signal fidelity - getting the data right - while neglecting semantic stability - ensuring that interpretation does not collapse under pressure.

Transparency without governance is simply a higher-resolution view of our own confusion.

7. An open research problem

The Hazard Semantics manuscript is deliberately diagnostic. It defines a failure mode and establishes a field capable of studying it.

A detailed preliminary governance framework exists to bound how meaning formation may and may not operate under multi-domain pressure, but it is intentionally not expanded here. Within those bounds, what remains unresolved is how semantic states should be examined, constrained, or refused as a formal operation, without collapsing into authority or action.

One open problem appears central: the formalization of refusal logic - the conditions under which a semantic system must enter a null, suspended, or indeterminate state rather than emit a fused meaning. This problem arises within an already-defined interpretive boundary, not in the absence of one.

Addressing this likely requires formal methods, state-space reasoning, and logics beyond the scope of a single author. I am interested in collaborating with researchers who want to formalize these conditions rigorously.

Closing

Semantic hazards are not hypothetical future risks. They already shape how institutions, AI systems, and the public interpret complex conditions.

If meaning itself remains unexamined, correctness at the data level will not prevent failure downstream.

Hazard Semantics exists to name and study that problem - so that governance can eventually be built on solid ground.

Full manuscript:
https://doi.org/10.5281/zenodo.17873981