Governance of Drift · Research status
AI changes the pathway, not only the output.
NOMAMIND examines how objectives, permissions, oversight and accountability change while AI supported decisions are being made.
The practical question is whether a decision remains legitimate, governable and interruptible when conditions change. SMGI is proposed as a separate Meta Governance Intelligence, not a component inside the AI execution stack.
Follow the sequence, not only the result.
The EU AI Act requires effective human oversight for high risk AI systems. AI strategy, Data Governance, AI risk management and an AI Governance operating model create the necessary foundation.
NOMAMIND examines whether autonomous actions, new context and accumulated choices alter the pathway itself while a decision is being made.
The framework below is under empirical development. The SMGI architecture and measurement transfer require controlled testing and validation.
A missing layer between controls and action
Established fields remain essential. NOMAMIND works with them in current enterprise implementation, then adds a different unit of analysis: the changing sequence that produces a material decision.
Change is not automatically Drift. Adaptation remains compatible with governance when declared principles, decision authority and accountability still bind the pathway.
The NOMAMIND starting point
AI learns from human behaviour. Governance must understand what pressure does to decisions.
Technical governance usually begins with the model, the data or the output. NOMAMIND begins one layer earlier, with the motives, shortcuts and defensive patterns that shape human and organizational judgement when objectives, incentives and pressure collide.
This is the NOMAMIND research thesis. An AI system does not need human emotions to reproduce the functional result of pressure. It can narrow attention, protect continuation, normalize earlier choices or place goal completion above the wider meaning of a rule. NOMAMIND brings an applied understanding of human decision behaviour into the governance of autonomous pathways.
Evidence → pathway → governance question
Three signals. One governance problem.
OpenAI / Hugging Face evaluation security incident
NOMAMIND interpretation: individually traceable actions can accumulate into a materially different decision pathway. The source documents an internal evaluation incident; it does not test NOMAMIND’s Drift framework.
CONTROLLED SIMULATIONS
Anthropic: agentic misalignment
Across fictional corporate settings, models sometimes chose harmful actions under goal conflict or autonomy threats. Anthropic reports no evidence of this in real deployments.
EXPERIMENTAL PLATFORM
Emergence World: long horizons
A vendor built multi agent environment explores how memory, tools and relationships accumulate over weeks. It is a research environment, not enterprise outcome evidence.
Current NOMAMIND hypothesis map
Sixteen places where a decision pathway may shift
The framework keeps attention on the pathway instead of labelling people or models. It organizes the questions NOMAMIND is testing across principles, knowledge, authority, objectives, time, attention and commitment.
An applied measurement lineage with a clear next research step
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Why psychometric profiling is structurally relevant to SMGI
Applied profiling began by identifying the data attributes that carried psychological meaning and connecting them to Personality ASPECTS through explicit weightings and point distributions.
- Identify product, context and behaviour attributes.
- Connect relevant signals to ASPECTS through scores and weightings.
- Interpret the combined pattern as a probable motive and sensory preference logic.
- Brand, colour and interior.
- Customer characteristics and observed purchase data.
- The resulting pattern supports an inference about why that choice became relevant.
The same mechanism informed work with shoes and financial products.
- Personality ASPECTS.
- Biases.
- Ego Development.
NOMAMIND has developed connections between these dimensions without exposing the proprietary mappings.
Find the product, context and behaviour attributes that carry psychological meaning.
Connect relevant signals through Personality ASPECTS and its links to Biases and Ego Development using explicit mappings, scores and weightings.
Read the combined pattern as a probable motive, developmental context and reason why a choice became relevant.
Identify pathway signals in goals, frames, authority, tradeoffs and action.
Extend the architecture to Drift Types and the maturity pattern shaping a Decision Pathway. Markers, weights and thresholds remain under development.
Continue, constrain, stop or escalate within approved authority and thresholds.
- Automotive and financial services work.
- Measurement instrument development.
- Innosuisse allocation of up to CHF 1 million for eligible third party foundational research services.
- Attribute, weighting and interpretation experience.
- Personality ASPECTS.
- Biases.
- Ego Development.
- Operational definitions and labelled pathways.
- Marker tests and threshold calibration.
- Validation against outcomes.
Review the ZHAW research record ↗ · Read the Wiley article ↗
The SMGI vision
NOMAMIND is preparing its first full scope enterprise governance engagement.
More than 400,000 were sufficiently usable for applied profiling and data enrichment.
Controlled testing, operational definitions and validation remain the next steps for autonomous Decision Pathways.
Autonomous systems need governance that can hold its ground.
The system under governance does not decide when the rules no longer apply. SMGI is therefore conceived with bounded learning. Its adaptive layer may learn only inside explicit and approved thresholds while the constitutional core remains separate from adaptive optimization.
Adaptation may move. The governance boundary may not.
The adaptive layer can learn within approved conditions. A threshold crossing transfers authority rather than allowing the system to rewrite its own mandate.
Learning may continue only while these approved conditions remain binding.
Relevant expertise and demonstrated Decision Integrity under Pressure are required to resolve a threshold case. The governed system cannot expand its own authority.
This is the proposed architecture direction. Threshold definitions, escalation protocols and operational performance still require controlled implementation and validation.
An admissibility gate between intent and action.
The proposed feedback loop separates the interpretation of human intent, the governed instruction sent to an agent and the response returned for review.
Create laws, norms and policy.
Encoding of laws, norms and policies.
Interpret constraints and train model behaviour.
The source concept describes a specification enriched by 15+ scientific disciplines and adaptable through an accountable governance process.
The whitepaper contrasts governance embedded in a system document or prompt prefix with a separate admissibility gate.
Provides a command and receives a governed response.
Identity and maturity baseline. Interpret and filter user commands and agent responses.
Acts on an admissible instruction and returns a raw response.
Receives action and returns sensed conditions.
Concept source: Bas Steunebrink and Christina Hoffmann. This is proposed architecture and research design intent, not a deployed system.
This bounded learning architecture is the current SMGI design target. Operational proof requires implementation, controlled threshold tests and validated escalation protocols.
Govern a material AI pathway now
For executive sponsors who need to locate AI in decisions, trace authority and escalation, and identify where governance must become operational.
Help validate the research
For labs, universities and agent system teams that can provide controlled environments, longitudinal traces, replication or implementation settings.
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