FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings
For researchers and practitioners in low-resource healthcare settings, FairHealth offers a unified toolkit addressing gaps in fairness, privacy, explainability, and data access, though it is an incremental integration of existing methods.
FairHealth is an open-source Python library for trustworthy healthcare AI in low-resource settings, integrating fairness auditing, privacy-preserving federated learning, explainability, and Global South datasets. It provides six modules built from five peer-reviewed contributions, all publicly available without data use agreements.
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 (fairhealth.federated), intersectional fairness metrics (fairhealth.fairness), hybrid fuzzy-SHAP explainability (fairhealth.explain), multilingual dengue triage (fairhealth.lowresource), equitable disaster aid allocation (fairhealth.equity), and public dataset loaders (fairhealth.datasets). All datasets used are publicly available without institutional data use agreements. FairHealth is installable via pip install fairhealth(PyPI: pypi.org/project/fairhealth/) and available at https://github.com/Farjana-Yesmin/fairhealth.