Abstract:We present FairHealth, an open-source Python library that provides a unified, modular framework for trustworthy machine learning in healthcare applications, with particular focus on low-resource and low-income country (LMIC) settings such as Bangladesh. FairHealth addresses four critical gaps in existing healthcare AI toolkits: (1) the absence of integrated fairness auditing for biosignals and clinical tabular data; (2) the lack of privacy-preserving federated learning tools compatible with standard ML workflows; (3) missing explainability tools tailored for low-bandwidth clinical decision support; and (4) no existing toolkit covering Global South healthcare datasets. Built from five peer-reviewed research contributions, FairHealth provides six modules covering federated learning with homomorphic encryption (this http URL), intersectional fairness metrics (this http URL), hybrid fuzzy-SHAP explainability (this http URL), multilingual dengue triage (this http URL), equitable disaster aid allocation (this http URL), and public dataset loaders (this http URL). All datasets used are publicly available without institutional data use agreements. FairHealth is installable via pip install fairhealth(PyPI: this http URL) and available at this https URL.
| Comments: | 8 pages, open-source Python library |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2605.08198 [cs.LG] |
| (or arXiv:2605.08198v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.08198 arXiv-issued DOI via DataCite |
Submission history
From: Farjana Yesmin [view email]
[v1]
Tue, 5 May 2026 20:55:39 UTC (9 KB)