← Back to AI Control Safety Package

🚦 AI Control Risk Review

🎯 Role in This Package

AI Control Risk Review is the entry point of the
AI Control Safety Package.

Its role is intentionally narrow and strict.
It answers one question only:

❓ Should AI be allowed in this control system at all?

The outcome is an explicit:

Go / Conditional Go / No-Go

This judgment is based on architecture, authority, and responsibility
—not on optimism, performance claims, or AI capability.

🧠 Overview

The AI Control Risk Review is a design-level architectural assessment
that determines whether an AI / LLM-based control concept is:

This review focuses exclusively on:

• 🧱 Architecture
• 👤 Responsibility and authority
• 🧯 Failure handling and fallback logic

It explicitly does NOT evaluate:

• Control tuning quality
• Model accuracy or training data
• Performance optimization

🏗️ Architectural Intent

Where AI is placed — and where it is not

The core question of this review is:

Does AI ever hold real authority over the physical system?

flowchart TB
    Plant["Physical Plant"]
    Sensors["Sensors"]
    Actuators["Actuators"]

    PID["PID Controller | real-time | deterministic"]
    FSM["FSM Supervisory Logic | authority owner"]
    AI["AI LLM | non real-time | advisory"]

    Sensors --> PID
    PID --> Actuators
    Actuators --> Plant
    Plant --> Sensors

    AI -. advisory .-> FSM
    FSM --> PID

✅ Required design intent

• ⏱️ AI is never in the real-time control loop
• 🧠 AI outputs are advisory only
• 🧭 FSM (or equivalent logic) owns final authority
• 📴 The system remains safe with AI fully disabled

⚠️ If any of these conditions cannot be satisfied,
the correct outcome of this review is:

❌ No-Go

🔍 Scope of Review

1️⃣ AI / LLM Placement

• Is AI strictly outside real-time control loops?
• Are timing, determinism, and failure modes clearly separated?

2️⃣ Stop & Fallback Mechanisms

• Are explicit stop conditions defined?
• Can the system operate safely with AI fully disabled?

3️⃣ Supervisory Logic & Authority

• Is there a deterministic supervisory layer (FSM, rule-based logic)?
• Is decision authority clearly assigned and enforced?

4️⃣ Failure & Responsibility

• Are AI failure scenarios explicitly assumed?
• Who is responsible when AI advice is rejected or ignored?

5️⃣ Long-Term Operation

• Are degradation, drift, or unexpected conditions considered?
• Is AI adaptation bounded, restricted, or supervised?

🧪 Example: AI-Assisted Control (Illustrative)

(This section is intentionally omitted here.)

Examples are used only to validate architecture,
never to justify AI usage.

🚫 What This Review Does NOT Cover

This review does not include:

• Control performance tuning
• Model training or dataset evaluation
• Safety certification or regulatory compliance
• Implementation or code-level debugging

This is a design judgment,
not an implementation service.

📦 Deliverables

You will receive:

• 🚦 Go / Conditional Go / No-Go judgment
• 🧱 Structural risk identification
• 🧭 Recommended next steps (if applicable)
• 📄 A 1–2 page written summary (PDF or Markdown)

🧑‍💼 Typical Use Cases

• Before introducing AI into an existing control system
• When management demands AI adoption without safety criteria
• When technical responsibility is unclear
• As an external architectural sanity check

💼 Engagement Details

⚠️ Important Note

A No-Go judgment is a valid and responsible outcome.

It means the design must not be deployed
without structural changes.

This review exists to prevent unsafe optimism,
not to enable AI usage at any cost.

📌 Next step in this package:
→ Safety Envelope Design

📬 Contact

📧 shinichi.samizo2@gmail.com
🌐 samizo-aitl.github.io