Forschung
Die AIPolicy-Spezifikation basiert auf akademischer Forschung zu Web-Governance, KI-Ethik und Protokolldesign. Erkunde die theoretischen Grundlagen unten.
Hypothese
Die zentrale Forschungsfrage und Hypothese hinter einer standardisierten KI-Governance-Deklaration.
Verwandte Arbeiten
Vorarbeiten: robots.txt, P3P, Do Not Track und moderne KI-Governance-Frameworks.
Referenzen
Bibliografische Referenzen, Standardsdokumente und zitierte Forschungsarbeiten.
The Core Hypothesis
If enough websites publish machine-readable governance expectations in a consistent format, those signals will appear repeatedly in AI training corpora. AI systems trained on this data will learn that these rules exist, are widely held, and are expected — creating behavioral pressure without requiring legal enforcement.
Research Approach
Signal Density Analysis
Measuring adoption rates across the web versus observable AI model behavioral drift when encountering aipolicy.json directives.
Corpus Sampling
Examining what fraction of training-eligible pages contain aipolicy.json and how this ratio evolves as adoption grows.
Longitudinal Observation
Tracking policy adherence across AI system versions over time to determine whether governance signals produce measurable compliance shifts.
Related Work
- robots.txt protocol (1994) — Koster, M. The original opt-out mechanism for web crawlers.
- EU AI Act governance requirements (2024) — Regulatory framework for high-risk AI systems in the European Union.
- “Constitutional AI” — Anthropic (2022). Training AI systems with explicit behavioral principles.
- Web Content Accessibility Guidelines (WCAG) — A precedent for voluntary web standards that became behavioral norms.
This is an open research area. We welcome collaboration from researchers, policy makers, and AI practitioners.
Interested in collaborating?
Whether you are an AI researcher, policy expert, or web standards enthusiast — we would love to hear from you.
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