This paper considers the cultivation of ethical identities among future engineers and computer scientists, particularly those whose professional practice will extensively intersect with emerging technologies enabled by artificial intelligence (AI). Many current engineering and computer science students will go on to participate in the development and refinement of AI, machine learning, robotics, and related technologies, thereby helping to shape the future directions of these applications. Researchers have demonstrated the actual and potential deleterious effects that these technologies can have on individuals and communities. Together, these trends present a timely opportunity to steer AI and robotic design in directions that confront, or at least do not extend, patterns of discrimination, marginalization, and exclusion. Examining ethics interventions in AI and robotics education may yield insights into challenges and opportunities for cultivating ethical engineers. We present our ongoing research on engineering ethics education, examine how our work is situated with respect to current AI and robotics applications, and discuss a curricular module in “Robot Ethics” that was designed to achieve interdisciplinary learning objectives. Finally, we offer recommendations for more effective engineering ethics education, with a specific focus on emerging technologies.
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AI in Health: State of the Art, Challenges, and Future Directions
Introduction: Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health. Objective: The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future. Methods: Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed. Results: The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed. Conclusion: Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias.
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- Award ID(s):
- 1650723
- PAR ID:
- 10157506
- Date Published:
- Journal Name:
- Yearbook of Medical Informatics
- Volume:
- 28
- Issue:
- 01
- ISSN:
- 0943-4747
- Page Range / eLocation ID:
- 016 to 026
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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