FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings

FairHealth is an open-source Python library designed to address critical gaps in healthcare AI for low-resource settings, particularly in low-income countries. The toolkit integrates fairness auditing, privacy-preserving federated learning, explainability tools, and Global South datasets into a unified framework, making trustworthy AI more accessible to underserved healthcare systems.

FairHealth tackles a significant bottleneck in global health technology: the absence of AI tools designed specifically for resource-constrained environments. While machine learning applications in healthcare have proliferated in wealthy nations, developing countries face compounded challenges—limited computational infrastructure, sparse training data, connectivity constraints, and linguistic barriers. This library directly addresses these structural inequities by bundling five peer-reviewed research contributions into an accessible pip-installable package.

The toolkit's architecture reveals thoughtful engineering for real-world deployment. The federated learning module with homomorphic encryption enables collaborative model training without centralizing sensitive patient data, addressing privacy concerns critical in regions with weak regulatory enforcement. The fuzzy-SHAP explainability approach prioritizes low-bandwidth interpretability, acknowledging that clinical decision support in rural Bangladesh differs vastly from connected urban centers. Notably, FairHealth includes intersectional fairness metrics specifically designed to detect algorithmic bias across demographic subgroups—a concern that often goes unaudited in Global South implementations.

For the broader healthcare AI ecosystem, FairHealth establishes a precedent for intentional geographical diversity in toolkit development. Rather than retrofitting Western-designed systems, the library embeds equity principles at its foundation. The inclusion of public datasets removes institutional gatekeeping, democratizing access to training resources. This approach reduces friction for researchers and practitioners in low-income settings who previously faced data acquisition barriers.

The library's impact depends on adoption velocity and continued maintenance. Its open-source nature creates potential for community-driven expansion, particularly if local healthcare technologists contribute domain-specific modules. Watch for institutional adoption by WHO-affiliated programs and NGOs implementing AI-assisted diagnostics in underserved regions.

• →FairHealth provides integrated fairness, privacy, and explainability tools specifically engineered for low-resource healthcare settings, filling a critical market gap.
• →The library bundles federated learning with homomorphic encryption and multilingual support, enabling collaborative ML development without centralizing sensitive patient data.
• →Open-source availability via PyPI and GitHub removes institutional barriers, democratizing access to trustworthy healthcare AI for Global South practitioners.
• →Intersectional fairness metrics and low-bandwidth explainability address algorithmic bias and deployment constraints endemic to resource-limited healthcare systems.
• →Community-driven development potential suggests the toolkit could become a standard reference implementation for equitable AI in international health programs.