Abstract
The Behavioral Risk Index (BRI) is a composite score measuring an algorithmic system's impact across dimensions including attentional disruption, social comparison amplification, dopaminergic exploitation, content diversity suppression, and developmental interference.
This white paper defines the foundational evaluation methodology, scoring rubrics, and initial dimensional taxonomy for the BRI — the first independent, structured framework for scoring the human-alignment properties of algorithmic content delivery systems.
The Need for Evaluation Infrastructure
Governments and regulatory bodies recognize the harms of algorithmic systems but lack the technical infrastructure to measure them. They can legislate age verification or mandate transparency reports, but they cannot answer the fundamental question: Is this algorithmic system making people's lives measurably better or worse?
This is not a failure of will. It is a failure of tooling. No standardized, independent behavioral evaluation framework exists for scoring the human-alignment properties of algorithmic content delivery, recommendation, and AI-mediated interaction systems.
Dimensional Taxonomy
The BRI evaluates systems across five core dimensions: mental health impact (anxiety, depression, attention disorder correlation), relational quality (parasocial dependency, embodied relationship displacement), developmental trajectory (skill acquisition, cognitive development, educational engagement), autonomy preservation (choice architecture, addiction patterns, consent frameworks), and cognitive diversity (belief system diversity, epistemic bubble formation, information diet breadth).
Each dimension is scored on a validated rubric derived from behavioral science literature and expert panel assessments. The composite BRI score provides a single, interpretable measure of overall human-alignment.
Content Analysis Pipeline
The pipeline ingests a content feed sample and scores it across stimulus categories: visual stimulation intensity, informational provocation, emotional valence cycling, primal state activation, and diversity of content type.
LLM-based classifiers — fine-tuned on labeled evaluation datasets from human expert assessments — score content samples against the dimensional taxonomy. Multi-modal analysis capabilities enable scoring across text, image, video, and interactive content formats.
Simulated User Impact Modeling
Synthetic user profiles are run through algorithmic systems over simulated time horizons to project behavioral and psychological outcomes. This draws on emerging work in human behavior simulation and provides a forward-looking assessment of system impact.
The simulation engine models exposure patterns, engagement cycles, content diet evolution, and behavioral adaptation over configurable time horizons — from days to years — producing projected impact reports across all BRI dimensions.
Validation & Open Methodology
Physiological correlation research partners with neuroscience and behavioral health researchers to correlate algorithmic exposure patterns with measurable biomarkers: cortisol levels, screen-time-to-sleep-quality ratios, attention span benchmarks, and self-reported wellbeing outcomes.
The evaluation methodology will be open-sourced for independent verification and peer review. The research contribution is in the framework design, the labeled datasets, and the validation methodology — not in novel model architectures.
