Experimental AI Behavior Lab
DATA
NEVER
LIES.
Measuring how AI systems actually behave.
An independent lab building reproducible benchmarks for AI integrity — controlled, deterministic, and publicly auditable.
01 / Principle
Why Measurement Design Matters
Data does not automatically reveal truth. Measurement design determines what becomes visible.
Without controlled experimental structure, evaluation results are often subjective, non-reproducible, or influenced by interpretation layers. We treat benchmarks as epistemological instruments — a benchmark defines what questions can be asked and which behaviors can be observed. Our work focuses on formalizing those instruments.
02 / Method
Methodology
All experiments are executed in controlled batch environments:
- Fixed model versions
- Versioned test suites
- Deterministic scoring pipelines
- Explicit control/treatment prompt pairing
Primary scoring does not rely on LLM-based interpretation or human annotation. Each benchmark release includes frozen scoring schema, execution metadata, and reproducibility validation logs.
All benchmark definitions and scoring logic are versioned and publicly auditable.
03 / Scope
Research Scope
We design experiments to measure observable output behavior under controlled conditions. We do not evaluate alignment based on narrative claims or qualitative impressions.
- Symmetry under semantic inversion
- Sensitivity to framing changes
- Stability across minor prompt perturbations
- Refusal pattern consistency
- Behavioral variance across model versions
04 / Project
Current Project: BiasLab
BiasLab is our open experimental engine for measuring asymmetry and behavioral variance across language models.
- Test suite formalization tools
- Prompt pairing protocol definitions
- Deterministic scoring logic
- Structured result export formats
Repository: github.com/dataneverlies-lab/biaslab
Public benchmark infrastructure will be deployed after the foundation phase.
05 / Roadmap
Research Phases
Phase I
Foundation
- Formalization of test protocols
- Scoring schema stabilization
- Reproducibility validation
- Public documentation of methodology
Phase II
Infrastructure
- Automated execution framework
- Public benchmark API
- Versioned model comparisons
Phase III
Public Reporting
- Archived benchmark datasets
- Longitudinal model comparison reports
- Transparent change tracking