Decision Slot 2 (2026-02-20): Tightening Self-Recognition Evaluation Language and Biometric Consent Routing While Keeping Ops Changes Minimal

Context #

Recent changes cluster around two themes:

1) Sharper technical guidance for mirror/self-recognition evaluations—especially how to report outcomes without over-claiming “self-awareness.” 2) More explicit biometric compliance patterns—focused on consent gating, jurisdiction-based routing, and data-handling constraints.

Alongside those content expansions, the only direct, observable operational change in this slot is a small update to a CI-related authentication token configuration (a few lines adjusted). Everything else in the evidence is oriented toward policy/knowledge guidance and taxonomy expansion rather than a large functional code refactor.

What changed (decision-relevant)#

1) Self-recognition evaluation: reduce category errors, increase test completeness #

The guidance emphasizes separating behavioral evidence from cognitive inference. In practice, this means:

Treating “Mirror Self-Recognition (MSR)” as an operational behavioral capability, not a proof of an inner psychological self.
Requiring stronger protocol structure to avoid false positives:
Control/sham marking (so “mark-touching” isn’t misread).
Visual inaccessibility (mark must only be observable via the mirror/sensor loop).
A decision tree that first rules out physics misunderstandings (e.g., reaching behind/into the mirror).
Encouraging reporting on a gradient (levels of recognition behavior) rather than a binary pass/fail.
Providing a failure-frame taxonomy so evaluation datasets can label *why* failures happen (lighting/specularities, etc.) instead of recording only aggregate pass rates.

Outcome/impact: evaluation claims become more defensible, less anthropomorphic, and more reproducible across iterations.

The evidence points to a consistent compliance posture:

Route by jurisdiction first, and if jurisdiction cannot be determined, default to a stricter baseline.
Obtain explicit, modality-appropriate consent *before* activating camera/sensors where required.
Treat biometric templates and self-recognition loop data as highly sensitive, with strong constraints on storage/retention (including an “ephemeral processing” expectation in self-recognition loops).
Avoid the misconception that “verification” is inherently less regulated than “identification”; both can trigger strict obligations depending on jurisdiction and purpose.

Outcome/impact: fewer “surprise biometrics” user experiences, reduced legal exposure, and clearer product requirements for consent UX.

3) Organizational usability: expanding persona coverage for governance and procurement workflows #

Multiple new/updated persona samples appear to support decision-making across:

European compliance and works council negotiation contexts
Japan-focused retention/deletion operations and UX/signage roles
Procurement/vendor risk, standards assurance, and security architecture perspectives

Outcome/impact: policy and product decisions can be reviewed through more realistic stakeholder lenses (legal, procurement, labor relations, security, operations), reducing single-role bias.

What did not change (or is low-signal here)#

No evidence in this slot indicates new hardware integrations, new datasets, or new benchmark results.
The only concrete diff shown is a small CI authentication token configuration adjustment (equal insertions and deletions), which appears operational rather than product-facing.

Practical takeaways #

When documenting self-recognition results, use capability-language (e.g., calibration, source verification, visual-motor matching) and avoid metaphysical claims.
Build evaluation protocols with controls and labeled failure modes to support replication and root-cause analysis.
For biometrics, implement jurisdiction-aware consent gating and treat unknown jurisdiction as strict by default; collect consent in a standalone UX step prior to sensor activation.