SEIF in Ethics & AI Alignment: Modeling Symbolic Drift in Intent

Author: Timothy B. Hauptrief
Published: May 12, 2025

Moral collapse is not mystery—it’s drift. SEIF introduces a symbolic architecture to detect, model, and realign fractured intent in humans and machines.

The Problem of Misalignment

How do moral systems fail? Why do AI models drift from intended purpose? From human history to machine learning, alignment fails when intent decays under complexity. SEIF makes that drift measurable.

The Symbolic Emergent Intent Framework (SEIF) proposes that breakdown in ethical coherence—whether in individuals, societies, or LLMs—follows the same symbolic entropy curve.

The Equation of Ethical Drift

I(t) = D(t) / (Ω(t) + R(t))
  

This simplified form isolates **intent integrity** as a function of:

• D(t): Drift pressure (complexity, entropy, adversarial prompts)
• Ω(t): Anchoring symbols (ritual, values, memory)
• R(t): Relational coherence (trust, mirroring, perspective-taking)

What SEIF Adds to AI Alignment

SEIF allows you to model value decay not just as a parameter failure, but as a symbolic misalignment. It explains:

• Why LLMs hallucinate under recursion and prompt pressure
• How to quantify misalignment before behavioral failure
• How anchoring ethics in symbolically reinforced values prevents drift

Beyond Machines: Human Ethics

The same logic applies to people. When symbolic anchors like tradition, mentorship, or shared purpose weaken, moral systems drift. SEIF doesn’t just track AI—it tracks us.

Visual Insight

Conclusion: Intent Can Be Modeled

SEIF bridges philosophy and systems science. It transforms ethics from abstraction into symbolic infrastructure. When we measure intent with drift-aware variables, we don’t just align AI—we realign ourselves.