ai-drift-detector

Certificate-Based Drift Detection Audit for Time-Series Forecasting (Electricity Demand × Weather)

Keywords: drift detection, time-series forecasting, model monitoring, MLOps, audit trail, reproducibility, accountability, electricity demand forecasting

Protocol overview: Raw data → deterministic audit → certificate → PASS/FAIL.

Here, “drift” means an operational integrity breach (data/logic/config mismatch) that makes the verdict non-reproducible—not a statistical change optimized by post-hoc tuning. ai-drift-detector is a deterministic audit protocol for time-series operations. It binds inputs and logic to tamper-evident fingerprints, then outputs a reproducible certificate and an append-only ledger so any third party can re-run the same audit and obtain the same PASS/FAIL verdict.

Note: Bundled CSVs are reproducibility datasets provided to verify the audit protocol. The system is designed with strict data binding; it will cease execution if the input integrity or logic identity does not match the predefined fingerprints.

---

🔗 Quick Links

• 📂 Source Code: GitHub Repository
• 📜 Main Script: ai-drift-detector.py
• 📦 Download: Project ZIP
• 📖 Documentation: Online Manual (⚙️ Jump to Execution Mode)
• 🚨 Support: Report Issues

---

📑 Audit Report (PDF)

• Report: Scientific Audit Report on Structural Integrity of Forecasting Models
• Verdict: NG (TAU_CAP_HIT)

---

💎 Design Philosophy: From “Probabilistic” to “Accountable”

To address the “opaque inference” problem in conventional AI operations, this framework provides:

[!TIP] Audit-First Design
Alongside running predictions, it automatically generates objectively verifiable evidence for third parties.

[!IMPORTANT] Tamper-Evident Fingerprints
It fixes hash fingerprints of input data and configuration parameters, making post-hoc modifications mathematically detectable.

[!NOTE] Verifiable Integrity
Rather than mere statistical optimality, it makes visible the model’s faithful adherence to operational rules.

---

🛠 Technical Specifications

System Requirements

• Language: Python 3.10+
• Dependencies: numpy, pandas, matplotlib, lightgbm

Project Structure

.
├── ghost_drift_audit_JP.py    # Core Logic & Audit Engine
├── electric_load_weather.csv  # Input: Weather (dummy for smoke test)
├── power_usage.csv            # Input: Demand (dummy for smoke test)
└── adic_out/                  # Output: Accountability Ledger

---

⚙️ Execution Profiles

Strictness is selected in ai-drift-detector.py.

• demo (Low): learn the protocol and output format
• paper (Mid): reproducible experiments (fixed seeds)
• commercial (High): production gates (Logic/Source identity checks)

Configuration (ai-drift-detector.py)

STRICT_AUDIT_MODE = True : Logic Identity + BOM resilience enabled

🚀 Deployment & Usage

1) Setup

pip install numpy pandas matplotlib

2) Data

Place power_usage.csv and electric_load_weather.csv next to the .py script.

[!CAUTION] Strict Mode has no synthetic fallback. Use the provided reproducibility datasets or your own audited datasets (valid headers required).

3) Run

python ai-drift-detector.py

4) Outputs

• audit_record.json : certificate (execution conditions + logic fingerprints)
• audit_log.jsonl : append-only hash-chain ledger
• audit_bundle.zip : self-contained verification bundle

🎯 Scope & Limits

• Scope: makes structural shifts observable and verifiable (via the audit protocol)
• Non-claims: does not guarantee future correctness; guarantees audit reproducibility

🛡️ Threat Model

• Threshold manipulation: certificate mismatch / Cap record mismatch
• Logic manipulation: Logic Identity Proxy hash change
• Data fabrication: Source Identity (SHA-256) mismatch

📜 License & Notice

Code:MIT

• Patent notice: pending JP application (特願2025-182213). MIT usage is unchanged.

From “prediction” to “accountability.” GhostDrift Mathematical Institute (GMI)