Leveraging Data Ethics to Create Responsible Artificial Intelligence Solutions in Nursing: A Viewpoint (Preprint)

Abstract

<sec> <title>UNSTRUCTURED</title> AI ethics is gaining much recognition because of adverse outcomes or ethical concerns such as algorithmic bias, lack of transparency, trust, data security, and fairness. Interestingly, artificial intelligence technologies, specifically machine learning algorithms, are often the focal point for optimization and achieving ethical human-intelligent-like systems. However, these technologies are fueled by data. Data is hidden behind these complex systems and needs to come to the forefront regarding its ethical collection, processing, and use. Data ethics and its importance in attaining responsible artificial intelligence in healthcare and nursing via data ethical frameworks and strategies are introduced. Furthermore, the implications of data ethics for nurses are presented. A formal literature survey was employed to gather and analyze data from the perspectives of data ethical concepts and definitions, responsible artificial intelligence, and data-centric artificial intelligence in healthcare and nursing. Eight principles of data ethics are introduced for consideration. The data-centric artificial intelligence paradigm can support these principles via the ethical creation of artificial intelligence solutions that incorporate human-centered domain expertise to create high-quality, representative data. This engagement is essential in high-stakes healthcare settings to protect patients’ data privacy and health outcomes. In conclusion, four recommendations are presented to nurse leaders, educators, and researchers to position and empower them to engage in data ethics in artificial intelligence to create ethical, high-quality, pertinent datasets from which machine learning algorithms can learn patterns and relationships embedded in the data. </sec>

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