Decision Notes (2026-02-15): Tightening self-recognition evaluation and biometric consent routing

Context#

Recent changes concentrated on maturing a “self-recognition” knowledge base into something that can drive deterministic product decisions: (1) how to evaluate self-recognition claims without over-claiming “self-awareness,” and (2) how to route biometric consent and processing prerequisites across jurisdictions (EU, Japan, and selected US states), including a strict default when jurisdiction is unknown.

What changed#

1) Clearer separation: evaluation vs calibration vs decisioning#

The materials were expanded to prevent category errors—especially the common failure mode of treating a mirror-style success criterion as evidence of broad self-awareness. The updates emphasize:

• Reporting observable behavior separately from cognitive inference.
• Avoiding prohibited language (e.g., labeling systems as “self-aware”) and using narrower terms like self-recognition, calibration, or source verification.
• Measuring capability on a gradient rather than a binary pass/fail.

2) Stronger test validity requirements for self-recognition#

The evaluation guidance was reinforced around validity controls and confounds, including:

• Requirements such as mark visibility constraints and sham/control conditions.
• A decision-tree style approach to quickly distinguish physics/perception failures (e.g., treating reflections as literal objects) from higher-level recognition behaviors.
• A failure-frame taxonomy to make evaluation reports actionable (so “failure rate” isn’t a single opaque number).

3) Jurisdiction-aware biometric prerequisites, with deterministic routing#

Cross-jurisdiction prerequisites were consolidated into a routing-friendly structure designed to be applied before activating sensors or processing biometric templates. Key themes include:

• Treating biometric processing as highly regulated across regions, not assuming “verification” is materially less regulated than “identification.”
• Using stricter consent patterns where required (not relying on general terms acceptance).
• Defaulting to a strict global posture when the user’s jurisdiction cannot be determined.

4) Operationalization beyond privacy: lifecycle + incident posture#

Operational content broadened from pure compliance into deployment reality:

• Enrollment, verification, revocation, and audit workflows.
• Data minimization, retention/deletion decisions, and role-based artifacts.
• Threat modeling and incident response considerations for biometric self-recognition systems.

5) Environment and interaction safety: “mirror risk” mitigation#

Design guidance was added to reduce adverse or confusing interactions driven by reflective environments:

• Practical installation and inspection considerations (e.g., placement, lighting, reflective surfaces) translated into measurable facility checks.
• Boundary and escalation language to keep interactions non-clinical while still defining when to hand off to human support.

Why it matters#

• Fewer overclaims: Teams get safer wording and cleaner inference boundaries, reducing reputational and compliance risk from overstated “self-awareness” narratives.
• More reproducible evaluations: Stronger controls, taxonomies, and gradient-based reporting make results easier to compare and debug.
• Deterministic compliance behavior: Routing rules and consent prerequisites support consistent product behavior across regions, especially under uncertainty.
• Deployment-ready posture: Lifecycle operations and incident thinking reduce the gap between “policy” and real-world system operation.

Outcome / impact#

Overall, the updates move self-recognition work from loosely described evaluation and ad-hoc consent handling toward: (1) auditable, validity-aware evaluation artifacts, and (2) jurisdiction-sensitive biometric decisioning that can be implemented as deterministic routing rules.

No changes detected?#

Changes were detected for this date window; the work focused primarily on evolving decision-relevant guidance and operational prerequisites rather than introducing new models, datasets, or benchmarks.