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arxiv: 2605.08198 · v1 · submitted 2026-05-05 · 💻 cs.LG · cs.AI· cs.CY

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

Farjana Yesmin This is my paper

Pith reviewed 2026-05-12 01:09 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CY
keywords healthcare AIfairness auditingfederated learningexplainabilitylow-resource settingsopen-source libraryprivacy preservationGlobal South datasets
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The pith

FairHealth is an open-source Python library that unifies fairness auditing, privacy-preserving federated learning, and tailored explainability for healthcare AI in low-resource settings.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents FairHealth as a modular Python library designed to address gaps in trustworthy AI for healthcare, especially in low-income countries. It integrates fairness auditing for clinical data, privacy-preserving federated learning, tailored explainability methods, and support for relevant datasets. A sympathetic reader would care because this could make advanced ethical AI techniques more accessible to researchers and practitioners working with limited resources. The library is built by combining results from five earlier studies into six practical modules.

Core claim

FairHealth offers six modules in a single framework: federated learning with homomorphic encryption, intersectional fairness metrics for biosignals and tabular data, hybrid fuzzy-SHAP explainability, multilingual dengue triage, equitable disaster aid allocation, and public dataset loaders. These components, drawn from prior peer-reviewed work, are intended to enable trustworthy machine learning without requiring institutional data agreements or high-bandwidth connections.

What carries the argument

The unified modular framework consisting of the six FairHealth modules that combine fairness auditing, privacy-preserving techniques, and explainability for healthcare applications.

Load-bearing premise

The six modules derived from five prior peer-reviewed contributions can be successfully unified into one framework that fills the four gaps without introducing new limitations for users in low-resource settings.

What would settle it

A demonstration that the library fails to run on typical low-resource hardware or does not improve fairness outcomes in real LMIC healthcare datasets would challenge the central claim.

read the original 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 (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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript presents FairHealth, an open-source Python library offering a unified modular framework for trustworthy ML in healthcare with emphasis on low-resource LMIC settings such as Bangladesh. It claims to close four gaps in existing toolkits—integrated fairness auditing for biosignals and tabular data, privacy-preserving federated learning compatible with standard workflows, explainability tools for low-bandwidth clinical use, and coverage of Global South datasets—via six modules (federated with homomorphic encryption, intersectional fairness, hybrid fuzzy-SHAP, multilingual dengue triage, equitable disaster aid allocation, and public dataset loaders) assembled from five prior peer-reviewed contributions. The library is pip-installable and uses only publicly available datasets.

Significance. If the claimed unification succeeds without introducing prohibitive overhead, compatibility friction, or loss of individual module guarantees in low-resource environments, the library could meaningfully advance equitable healthcare AI by making fairness, privacy, and explainability techniques accessible for applications like dengue triage and disaster response in the Global South.

major comments (2)
  1. Abstract: The central claim that the six modules fill the four identified gaps via a single modular framework is load-bearing, yet the manuscript supplies no integration architecture, cross-module compatibility tests, or low-resource performance benchmarks; this leaves open whether unification adds hidden costs or breaks prior guarantees, as highlighted by the stress-test concern.
  2. Module descriptions: No empirical validation results or ablation studies are provided for the individual modules (e.g., federated learning with homomorphic encryption or hybrid fuzzy-SHAP) or their combined use on the cited applications, so effectiveness for LMIC users cannot be assessed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript describing the FairHealth library. The comments highlight important aspects of integration and validation that we will address in revision to better demonstrate the framework's utility for LMIC settings. We respond point by point below.

read point-by-point responses

  1. Referee: Abstract: The central claim that the six modules fill the four identified gaps via a single modular framework is load-bearing, yet the manuscript supplies no integration architecture, cross-module compatibility tests, or low-resource performance benchmarks; this leaves open whether unification adds hidden costs or breaks prior guarantees, as highlighted by the stress-test concern.

    Authors: We agree that an explicit integration architecture and compatibility details are needed to support the central claim. In the revised manuscript, we will add a new section (with accompanying diagram) describing the modular design, data flow between modules (e.g., applying fairness metrics within federated workflows and low-bandwidth explainability), and API compatibility guarantees. We will also include preliminary cross-module benchmarks from our development tests, including overhead measurements in simulated low-resource environments, to address potential hidden costs. These additions will clarify that prior module guarantees are preserved. revision: yes

  2. Referee: Module descriptions: No empirical validation results or ablation studies are provided for the individual modules (e.g., federated learning with homomorphic encryption or hybrid fuzzy-SHAP) or their combined use on the cited applications, so effectiveness for LMIC users cannot be assessed.

    Authors: The core empirical validations and ablations for each module appear in the five cited prior peer-reviewed works. For the current library-focused manuscript, we will revise the module sections to include concise summaries of key results (e.g., accuracy-privacy trade-offs for the homomorphic encryption federated module and fidelity metrics for hybrid fuzzy-SHAP). We will further add a dedicated case-study subsection showing combined module usage on the dengue triage and disaster aid allocation tasks, with metrics relevant to LMIC constraints. This will allow readers to evaluate effectiveness directly. revision: yes

Circularity Check

0 steps flagged

No significant circularity; library unifies prior contributions without self-referential derivations

full rationale

The paper presents FairHealth as an open-source Python library that integrates six modules drawn from five prior peer-reviewed contributions. No mathematical derivations, equations, fitted parameters, or predictive claims appear in the abstract or described structure. The central contribution is the existence and modularity of the library itself (installable via pip, hosted on GitHub), with claims about addressing four gaps resting on the prior works rather than reducing any new output to those inputs by construction. Self-citations to the author's earlier papers are present but function as references to independent modules, not as load-bearing justifications that collapse the library's novelty into a tautology. The unification claim is presented as an engineering integration rather than a formal derivation, leaving no steps that match the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a software library presentation with no new mathematical derivations, free parameters, or invented entities; it relies on standard machine learning practices and prior peer-reviewed work.

pith-pipeline@v0.9.0 · 5527 in / 1185 out tokens · 71131 ms · 2026-05-12T01:09:47.592822+00:00 · methodology

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    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).

  • IndisputableMonolith/Foundation/RealityFromDistinction reality_from_one_distinction unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    Built from five peer-reviewed research contributions... pip install fairhealth

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages

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