Model Evaluation and Governance
Semantic, place, object, and face retrieval are evaluated as separate systems. None inherits approval from another model.
Separate project gates
| Project | Primary evaluation | Release gate |
|---|---|---|
| Semantic similarity | Relevance, false association, demographic and cultural skew | Clearly labeled discovery results and benchmark publication |
| Place recognition | Geographic accuracy, sensitive-location leakage, regional coverage | Privacy review and country-level error reporting |
| Object recognition | Class precision, long-tail failures, trademark and product ambiguity | Confidence calibration and category exclusions |
| Face models | False-match rates by subgroup, pose, age, image quality, and region | Lawful-purpose controls, adult-only policy, audit logs, region restrictions, and independent review |
Evidence before launch
A versioned report must show accuracy, false associations, subgroup results, capacity, and known failure modes.
Control after launch
Every approved model needs monitoring, an accountable owner, a rollback path, and a dated re-evaluation plan.
Why the projects are separate
One approval cannot cover four systems
A model that performs well at finding visually related scenes does not establish that it can identify a place, object, or person reliably. Each project uses different training data, error definitions, abuse risks, and user expectations. Product copy, result labels, evaluation sets, and launch decisions must preserve those distinctions instead of combining unrelated scores into one broad claim about intelligence or recognition. The research methodology provides the evidence framework used around tool results.
Evaluation requirements
Every candidate system needs a versioned benchmark with representative positive and negative examples, documented dataset provenance, subgroup and regional slices where relevant, confidence calibration, latency and capacity tests, and a written analysis of false associations. Reviewers must test degraded inputs such as crops, compression, occlusion, unusual lighting, small subjects, and generated or edited media. Results should record the model version and must be reproducible before a release decision.
Privacy and abuse review
The review considers whether a feature could expose a home, sensitive institution, protected person, private collection, or identity relationship. Controls can include purpose restrictions, regional availability, rate limits, audit logs, query retention limits, opt-out handling, adult attestations, category exclusions, and human verification prompts. A technically accurate result can still be inappropriate to release if the likely harm cannot be reduced to an acceptable level. The privacy policy describes how current searches and saved records are handled.
Current status
These projects remain evaluation-only until their own benchmark, privacy, abuse, security, and operational gates pass. Each project has an independent production switch, and the server refuses that switch without a current approval tied to the exact model artifact and benchmark dataset.
Approval also requires named privacy, abuse, security, and capacity reviewers, a rollback owner, and legal review for a face model. Existing same-photo and near-duplicate search should not be described as identity recognition. A future status change will identify the approved model, evaluation date, permitted purpose, known limits, rollback owner, and monitoring plan.