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Ethical Issues in Informatics and Data Science

This methodological focus is driven by the need to understand and mitigate bias in informatics and Data Science Research. This thread will be a central part of our program’s engagement in the university-wide OneU initiative, which brings together experts from across the university to solve big problems in society. Trainees will be trained on Ethical Issues in Informatics and Data Science in collaboration with faculty in the Philosophy Department, Nursing Informatics, and the University of Utah Health (UUH) Office of the Chief Medical Information Officer (CMIO).

Artificial intelligence (AI) and Machine Learning (ML) technologies are becoming ubiquitous in health research and have the potential to transform the healthcare landscape. Developing educational programs covering AI is challenging due to the involvement of multiple technical and non-technical components. In the past few years, training opportunities on the technical aspects of machine learning, NLP, and other AI subdomains have increased within our department. However, there have been few training opportunities on the social, ethical, and philosophical implications of AI. Any intervention has the potential to increase health disparities and this is particularly true for AI. It has become increasingly clear that the use of AI in medicine and society involves many ethical and philosophical challenges. For example, researchers are increasingly worried about “algorithmic bias.” Such biases take various forms, and in some cases, developers’ background assumptions threaten to skew the programs they develop or implement. In others, algorithms may become biased through normal machine learning processes because they both track and replicate inequalities in society at large. If data scientists do not know how to detect bias in their algorithms or train algorithms in ways that avoid bias, they could unintentionally design and implement programs that promote injustice, including health inequalities. Understanding these issues is vitally important as we move our research and education forward.