What This Demo Shows

This prototype implements the clinical StARS profile. Each slider encodes a normalized 0–100 signal for either Agent Stress Load (ASL) or Code Vulnerability (CV). The engine computes:

• A composite ASL score from Burnout, Moral Injury, and Rule-Bending.
• A composite CV score from Protocol Complexity, Conflicting KPIs, Incidents, Policy Gaps, and Control Failures.
• The final StARS risk index (50/50 weighted ASL/CV) and the dominance differential Δ.

How to Use the Interface

1. Adjust the ASL sliders to match your survey or observational data.
2. Adjust the CV sliders based on structural metrics.
3. Optionally paste raw logs in the Quick Intake box using metric: value lines.
4. Click CALCULATE ALIGNMENT RISK.
5. Read the StARS Index, dominance regime, and correction path.

How an AI Model Would Work with StARS

In a full implementation, a hosted AI model would ingest unstructured data, classify each signal as ASL-type or CV-type, normalize those signals to 0–100, update ASL and CV continuously, and trigger alerts or routing decisions when Δ crosses the dominance threshold.

Educational & Research Use

The math and logic shown here may be used for teaching, experimentation, and research on systemic risk, burnout, and AI alignment.