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      The Ubuntu Way: Ensuring Ethical AI Integration in Health Research

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          Abstract

          The integration of artificial intelligence (AI) in health research has grown rapidly, particularly in African nations, which have also been developing data protection laws and AI strategies. However, the ethical frameworks governing AI use in health research are often based on Western philosophies, focusing on individualism, and may not fully address the unique challenges and cultural contexts of African communities. This paper advocates for the incorporation of African philosophies, specifically Ubuntu, into AI health research ethics frameworks to better align with African values and contexts.

          This study explores the concept of Ubuntu, a philosophy that emphasises communalism, interconnectedness, and collective well-being, and its application to AI health research ethics. By analysing existing global AI ethics frameworks and contrasting them with the Ubuntu philosophy, a new ethics framework is proposed that integrates these perspectives. The framework is designed to address ethical challenges at individual, community, national, and environmental levels, with a particular focus on the African context.

          The proposed framework highlights four key principles derived from Ubuntu: communalism and openness, harmony and support, research prioritisation and community empowerment, and community-oriented decision-making. These principles are aligned with global ethical standards such as justice, beneficence, transparency, and accountability but are adapted to reflect the communal and relational values inherent in Ubuntu. The framework aims to ensure that AI-driven health research benefits communities equitably, respects local contexts and promotes long-term sustainability.

          Integrating Ubuntu into AI health research ethics can address the limitations of current frameworks that emphasise individualism. This approach not only aligns with African values but also offers a model that could be applied more broadly to enhance the ethical governance of AI in health research worldwide. By prioritising communal well-being, inclusivity, and environmental stewardship, the proposed framework has the potential to foster more responsible and contextually relevant AI health research practices in Africa.

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          Most cited references59

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          Dissecting racial bias in an algorithm used to manage the health of populations

          Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.
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            The global landscape of AI ethics guidelines

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              Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations.

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Project AdministrationRole: SupervisionRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Journal
                Wellcome Open Res
                Wellcome Open Res
                Wellcome Open Research
                F1000 Research Limited (London, UK )
                2398-502X
                28 October 2024
                2024
                : 9
                : 625
                Affiliations
                [1 ]Strathmore University, Nairobi, Nairobi County, Kenya
                [2 ]School of Law, University of KwaZulu-Natal - Pietermaritzburg Campus, Pietermaritzburg, KwaZulu-Natal, South Africa
                [3 ]Egerton University, Njoro, Nakuru County, Kenya
                [4 ]Department of Global Health and Social Medicine, Bush House, North East Wing, Strand, King's College London, London, England, UK
                [1 ]University of the Cumberlands, Williamsburg, Kentucky, USA
                [1 ]Chicago State University, Chicago, USA
                [1 ]University of Toronto, Toronto, Ontario, Canada
                Author notes

                No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Author information
                https://orcid.org/0000-0002-4584-6873
                https://orcid.org/0000-0002-7568-2224
                Article
                10.12688/wellcomeopenres.23021.1
                11599802
                39606617
                008c07d1-89de-4f08-94b4-664d7d41f83d
                Copyright: © 2024 Odero B et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 October 2024
                Funding
                Funded by: Wellcome
                Award ID: [222180/Z/20/Z]
                This work was supported by Wellcome [222180/Z/20/Z].
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Review
                Articles

                ubuntu,health research,ethics,artificial intelligence (ai),africa

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