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Routine risk screening informs follow-up care, yet conventional approaches often fail to address cultural considerations. The Baby Coming You Ready (BCYR) program provides a strengths-based, culturally adapted digital assessment designed to enhance perinatal mental health care. However, some clinicians continue to rely primarily on conventional risk scores, which can neglect broader contextual circumstances influencing Aboriginal mothers, including cultural strengths and protective factors, thereby limiting the program\u2019s full impact. This exploratory model-development study examined whether generative AI data augmentation combined with an intrinsically interpretable machine-learning model could support culturally contextualised prediction of perinatal psychological distress among Aboriginal mothers. We analysed deidentified BCYR pilot data from 293 Aboriginal mothers across six Western Australian sites. After excluding five records with incomplete outcome data, the analytic dataset included 288 participants. Models were evaluated using repeated internal train-test splits, with synthetic data used only for training augmentation and performance assessed on held-out real test data. The highest internal performance was observed for the TVAE-augmented Explainable Boosting Machine at an augmentation ratio of 1.0, with an accuracy of 0.867, an F1 score of 0.817, and an AUC of 0.934. Model explanations identified self-blame, loneliness, family pride, day-to-day management, sleep, and related contextual factors as influential contributors to model-estimated risk. These explanations are intended as prompts for culturally safe discussion and clinical reflection. Findings suggest that interpretable generative AI may have potential to support culturally responsive perinatal mental health assessment. However, broader community-governed external validation and prospective clinical workflow evaluation are required before clinical implementation.<\/jats:p>","DOI":"10.1371\/journal.pdig.0001709","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T17:23:26Z","timestamp":1792085006000},"page":"e0001709","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["The world\u2019s oldest living culture leads the way with interpretable generative AI: Aboriginal women shaping the future of perinatal mental health services"],"prefix":"10.1371","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7536-5782","authenticated-orcid":true,"given":"Guanjin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Technology, Murdoch University","place":["Perth, Western Australia, Australia"]},{"name":"Ngangk Yira Institute for Change, Murdoch 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