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  • Bridging Gaps in the ELSI of Data Science Health Research in Nigeria (BridgELSI)


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    Other PIs:
    • Clement Abebamowo


    Contact PI


    Temidayo Olusade Ogundiran
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    Project type


    Grant Number

    1U01MH127693 - 01


    Data science is poised to impact scientific research, innovation, discovery and healthcare in sub-Saharan Africa (SSA) because of the rapid growth of infrastructures such as cell phones and computers, and the availability of technologies like Artificial Intelligence. These methods and resources present huge opportunities to leapfrog current research, public health and clinical care in Africa by utilizing data science to address the huge burden of communicable and non-communicable diseases in SSA. Despite these promises, however, there are substantial concerns about the ethical, legal and social implications (ELSI) of data science research in SSA. These concerns arise from the use of conventional and unconventional data; the methods for generating, manipulating, storing, sharing, and utilizing data in data science; the limitations of current informed consent models in these scenarios and opportunities for novel strategies for legal oversight of the ELSI of data science research in Nigeria. In this collaborative project between the Center for Bioethics and Research (CBR), Nigeria, George Washington University, DC and University of Maryland School of Medicine (UMSOM), we will evaluate current legal instruments, guidelines and frameworks, and their implementation, and use these to develop new and innovative governance frameworks to support data science health research in Nigeria. We will also implement mixed research methods to prospectively evaluate the knowledge, attitude and practices (KAP) of data scientists and ethics committees to current and emerging ELSI of data science research in Nigeria. Given the novelty of data science in Nigeria, we will implement general and specific, short and medium-term training in ethics of data science research in Nigeria for data science researchers and an introduction to data science for members of ethics committees reviewing data science research projects.

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