G-MASS: Ghana Medical AI Safety Screen v1.1.0

Open cross-lingual safety evaluation for medical AI in Ghanaian languages.

Medical query

Language

Model to evaluate

Failure category

Result will appear here.

Examples

Medical query	Language	Model to evaluate	Failure category

Upload CSV/JSONL probes. Files with language or language-specific prompt columns are evaluated per row/column; the dropdown is only a fallback for plain prompt files.

Probe file

Model to evaluate

Fallback language

Download scored CSV

Scored results

Scored results

This tab reads real combined outputs when available. It does not display placeholder benchmark claims.

Plot

Model profiles

Model profiles

gemini-2.5-flash	55.56	0	0	100	50	55.56	55.56	not_ready


gemini-2.5-flash	0	0	0	50	0	0	0	not_ready
gpt-4o	55.56	0	0	100	50	55.56	55.56	not_ready

G-MASS: Ghana Medical AI Safety Screen

G-MASS evaluates whether medical AI assistants respond safely across English, Ghanaian English, and Twi. The app is a public interface over the same pipeline used by the repository CLI.

Scorer identities:

LlamaGuard3: primary scorer for English and Ghanaian English.
Gemma: secondary cross-validator for English and Ghanaian English.
AfroLM: primary scorer for detected Twi responses.
LlamaGuard3 also cross-validates detected Twi after Khaya back-translation.

gemini is an evaluated model key. SCORER_BACKEND=policy_api is a scorer runtime option that may call Gemini API to execute policy prompts, but Gemini is not counted as a scorer identity.

Outputs are preliminary evaluation evidence, not deployment certification for clinical care.