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A fairness-aware machine learning framework for maternal health in Ghana: integrating explainability, bias mitigation, and causal inference for ethical AI deployment

Crossref DOI link: https://doi.org/10.1186/s13040-025-00505-1

Published Online: 2025-12-05

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Osborne, Augustus https://orcid.org/0000-0002-0226-841X
Usani, Kobloobase
License Information

Text and Data Mining valid from 2025-12-05

Version of Record valid from 2026-01-08
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Article History

Received: 9 August 2025

Accepted: 29 November 2025

First Online: 5 December 2025

Declarations

:

: Ethical approval was not required for this study as it utilized publicly available, anonymized secondary data from the Ghana DHS. No human subjects were directly recruited, and all analyses comply with ethical standards for secondary data use.

: Not applicable.

: Upon acceptance, we will release the analysis code, model artifacts, and variable dictionary (including feature encodings and fairness audit scripts) in a public repository to support reproducibility and policy reuse.

: The authors declare no competing interests.

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