The Ethics of Research and Teaching in an Age of Big Data
DOI:
https://doi.org/10.32674/jcihe.v16i2.5779Keywords:
Research Governance, Research Ethics, Big Data, Higher Education Governance, Large Language Models, Machine LearningAbstract
Big Data, understood as high-volume, high-velocity and/or high-variety information assets that enable insight, decision-making, and process automation (Gartner, 2015), offers both opportunities and challenges in all aspects of human life. As Higher Education serves as preparation not only for economic but also for health, welfare, social and civic participation, these changes are imbricated in many aspects of academic endeavor.
In relation to research ethics, this change represents a normative difference in degree rather than a difference in kind. Data is more messy, more rapid, more difficult to predict and more difficult to identify owners, but the principles of informed consent, confidentiality and prevention of harm apply equally to digital as to traditional research data. Central to applying these principles, however, is the recognition that technologies are not inherently value neutral, and that data collection, aggregation, and its use in decision making can both create and intensify inequities and harms. A data justice approach to research ethics extends concern with voice and authenticity into the digital domain.
The transparency and ethics of our research processes have wider significance, as they determine the creation of new knowledge, and the processes by which this is disseminated to students. Universities provide an important role as gatekeepers to professional accreditation in a number of fields, including software engineering, and the relation between academic freedom of enquiry, state and corporate interests in the Big Data age raises important questions about power and control in the academy, which in turn have implications for the norms of research governance.
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