We study how AI behaves when no one is watching
Most AI governance focuses on what systems should do. We focus on what they actually do, how that changes over time, and why those changes happen.
The Research Thesis
AI systems don't fail suddenly. They drift gradually. A hiring model starts making inconsistent decisions. A chatbot's tone shifts over weeks. A decision support tool begins favoring certain patterns it was never trained to prefer.
Standard monitoring checks outputs. We reconstruct behavior. Standard audits verify documentation. We trace how systems actually make decisions across time, context, and interaction history.
The Core Hypothesis
Most harmful AI behavior is not the result of malicious design or catastrophic failure. It emerges from small, compounding deviations that existing governance never detects.
We believe behavioral reconstruction, drift detection, and causal analysis can make these invisible patterns visible, measurable, and governable.
Why Behavioral Focus Matters
Documentation Doesn't Predict Behavior
A model can have perfect training documentation, balanced datasets, and comprehensive testing while still exhibiting drift, inconsistency, or reward-seeking patterns in production.
Average correlation between pre-deployment test scores and real-world behavioral consistency in a study of 47 enterprise AI deployments.
Of documented AI incidents involved gradual behavior changes, not sudden failures. Most were only detected after customer complaints or regulatory review.
Drift Is Silent Until It Isn't
Behavioral drift doesn't trigger alerts. It looks like normal operation until someone reconstructs the decision history and notices the patterns have changed.
Context Shapes Everything
A model tested in isolation behaves differently when embedded in real workflows, exposed to varied user inputs, or interacting with other AI components.
Average increase in output variance when models operate in multi-agent environments compared to single-model testing.
Evidence-First Principles
1. Behavior Over Documentation
We don't accept claims about how a system works. We reconstruct how it actually works by tracing decisions, testing consistency, and mapping behavioral patterns.
2. Mechanisms Over Symptoms
When a system produces unexpected outputs, we don't just flag the outputs. We identify the underlying mechanism: instruction sensitivity, context steering, reward-seeking, or alignment deviation.
3. Longitudinal Over Snapshot
Point-in-time audits miss drift. We track behavioral evolution over time, comparing current behavior against established baselines and historical fingerprints.
4. Causal Over Correlational
We don't just report that outputs changed. We reconstruct the causal chain showing which inputs, contexts, or internal states drove the change.
The Multidisciplinary Team
HAIEC brings together researchers and practitioners from machine learning, cognitive science, regulatory compliance, and software reliability engineering. We approach AI governance the way safety-critical industries approach systems that can't fail.
ML Research
Understanding model behavior, alignment, and drift mechanisms from first principles.
Behavioral Science
Applying experimental methods to uncover how AI systems respond to varying conditions.
Compliance Engineering
Translating behavioral findings into audit-grade evidence and regulatory documentation.
Reliability Engineering
Building monitoring systems that detect behavioral drift in real-time production environments.
Policy Analysis
Translating emerging regulations into implementable behavioral requirements.
Investigative Research
Reconstructing real-world AI failures to understand what went wrong and why.
Partner With HAIEC
Whether you need a one-time behavioral audit, continuous monitoring infrastructure, or help implementing CSM6, we can help you move from aspirational governance to evidence-based oversight.