ETHICS.md

Ethics & Inclusion Note

Consent. Audio is processed only after explicit spoken or written consent, captured in the language the patient speaks. Consent for care is not consent for data retention; these are asked separately.

Data minimisation. The intake record stores a pseudonymous queue reference, never a name. Audio is processed in-memory and not persisted by default. Any audio contributed to the optional benchmark set is de-identified and re-consented.

Safety over fluency. The system escalates on uncertainty and never de-escalates on model confidence. Every tier is marked as requiring clinician confirmation. The tool supports a triage nurse; it does not replace one.

The inequity we are measuring. ASR trained predominantly on Western English degrades on African-accented and code-switched speech. In a clinic that degradation is not a quality issue, it is a safety issue: the patients least well served by the model are those least able to switch into the model's preferred language. Our benchmark reports under-triage rate precisely because that is where this inequity becomes clinical harm. A model that performs well on WER while dropping negations and clinical entities is a model that will fail these patients quietly.

Known gaps. The red-flag lexicon lacks native-speaker clinical validation. The language-identification heuristic is lexicon-based. Neither is deployment-ready, and we state that rather than shipping confidence we have not earned.