Multilingual NLP for African Healthcare: Bias, Translation, and Explainability Challenges

Language technologies have advanced significantly, yet African languages remain underrepresented in natural language processing (NLP) and machine translation (MT) due to data scarcity, linguistic complexity, and computational constraints.Large-scale models such as No Language Left Behind (NLLB-200) and Flores-200 have made strides in expanding machine translation for low-resource languages, yet significant challenges persist in adapting them for healthcare and domain-specific applications in African contexts.This paper explores multilingual NLP and translation models in African healthcare, evaluating approaches such as Masakhane-MT for translation, Masakhane-NER for named entity recognition (NER), and AfromT for domain adaptation.Focusing on languages like Swahili, Yoruba, and Hausa, the evaluation highlights bias, linguistic inequity, and performance disparities through a literature review and analysis of existing models.Use cases such as Ubenwa's infant cry analysis for asphyxia diagnosis and translation models trained on Flores-200 benchmark datasets demonstrate both potential and limitations in real-world applications.Our findings underscore the need for culturally adapted, explainable AI systems that integrate linguistic diversity, ethical AI principles, and communitydriven data collection.Limitations include dataset quality concerns, bias in training corpora, and a lack of healthcare-specific benchmarks for African languages.We propose strategies for bias mitigation, improved dataset representation, and culturally aligned NLP models, with a focus on data accessibility, fairness, and equitable AI deployment in African healthcare.2 Literature Review Multilingual NLP and African HealthcareRecent advancements in multilingual NLP have significantly improved language translation and understanding across diverse linguistic landscapes.