Epistemic status: Practical framework developed from security/audit background, applied to human-AI interaction. Not empirically validated, but internally consistent and designed for real-world application.

The Core Problem

Human–AI interaction is no longer about "using a tool." It's about coordinating with an adaptive, semi-autonomous partner. As AI systems become more capable and agentic, the critical question shifts from "Does it perform well?" to "Can humans maintain meaningful oversight and agency?"

Current discussions of AI alignment focus heavily on training-time interventions (RLHF, constitutional AI, value learning) and behavioral constraints (filters, classifiers, refusal training). These are necessary but insufficient. They address what the AI does but not what the human experiences or understands about the interaction.

EIOC is a framework for the interface layer—the operational contract between human and AI that determines whether collaboration is safe, predictable, and empowering.

The Four Pillars

Explainability: "Tell me what you're doing and why."

Explainability is surface-level clarity—the AI's ability to articulate its reasoning, steps, or rationale in human-understandable terms.

What it enables:

Trust calibration—Users need to know when to rely on the AI and when to override it
Error detection—If the AI explains its chain of thought, humans can spot mismatches
Learning and co-adaptation—Users learn how to work with the system; the system learns what explanations resonate

Key insight: Explainability is not just a feature—it's a relationship practice. An AI that can't explain itself creates an asymmetric relationship where the human must trust blindly.

In practice:

"Here's why I recommended this."
"Here's the uncertainty in my answer."
"Here's what I assumed based on your input."

Explainability is the narrative layer of the interaction.

Interpretability: "Help me understand how you work under the hood."

Interpretability is deeper than explainability. It's the human's ability to form an accurate mental model of how the AI system behaves.

What it enables:

Predictability—Users can anticipate how the AI will respond
Mental model alignment—Humans and AI share a common operational vocabulary
Reduced cognitive load—When users understand the system's logic, they don't have to guess

Key insight: A system can be explainable but not interpretable. It can tell you what it did w...

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