Measuring how AI systems actually behave.
Independent experimental lab building reproducible benchmarks for AI integrity — using controlled test suites and deterministic scoring.
Public benchmark infrastructure will be launched after the foundation phase.
What we do
Dataneverlies is an experimental AI behavior lab. We design repeatable tests to measure structural properties of language models.
- Symmetry tests
- Framing sensitivity analysis
- Stability measurement
- Refusal pattern mapping
Why it matters
AI systems increasingly decide what information people can access and what paths are available to them. That means the behavior of these systems needs verifiable, reproducible accountability tests—not just intuition or marketing claims.
Our goal is to make model behavior measurable, comparable, and reproducible.
Method
Controlled prompt pairing — isolate variables
Deterministic scoring — no LLM-based interpretation
Sanity checks — reduce false positives
Versioned test suites — ensure repeatability
Current Project: BiasLab
BiasLab is our open experimental engine for measuring asymmetry and behavioral variance across language models.
Public benchmark dashboard will be deployed during the infrastructure phase.
Roadmap
- Foundation – methodology, open tools, reproducible runs.
- Infrastructure – automated test execution, public benchmark API.
- Public Reporting – public dashboard, archived datasets, version comparisons.
Contact
Dataneverlies.org
Experimental AI Behavior Lab