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Learning Engineering is Ethical
Published in Jim Goodell, Janet Kolodner, Learning Engineering Toolkit, 2023
Ethical AI. At a general level, UNESCO has developed recommendations for the ethics of artificial intelligence noting that “living in digitalizing societies requires new educational practices, the need for ethical reflection, critical thinking, responsible design practices, and new skills, given the implications for the labor market and employability.” 14 They identify four core values: Respecting, protecting, and promoting human dignity, human rights, and fundamental freedomsRecognizing and promoting environment and ecosystem flourishingEnsuring diversity and inclusivenessLiving in harmony and peace
Promoting Ethics in STEM And Society
Published in Evelyn Brister, Robert Frodeman, A Guide to Field Philosophy, 2020
Among practical efforts, professional societies have initiated joint activities to stimulate attention to ethics and STEM. For instance, in 2017 the NAE, the Institute of Electrical and Electronic Engineers (IEEE), and the American Association of Engineering Societies (AAES) sponsored a workshop on the ethics of artificial intelligence (NAE 2017). The National Academies Government– Industry–University Roundtable sponsored several workshops on ethical aspects of scientific practice, including international aspects (NRC 2014; NAS, NAE, and IOM 2011). NASEM has long paid attention to ethics in graduate education in the U.S. and, more recently, in an international effort (IAP 2016). Additional efforts, particularly focusing on scientific misconduct and good practice, may be on the horizon. Conceptual discoveries involving the examination of value dimensions in STEM such as the complexities of risk assessment and risk management, have considerable intellectual strength; results from this work require connections between philosophy and science and engineering and the promotion of attention to ethics in STEM education. Public attention stimulated many of these efforts. Not surprisingly, there is interaction between the stimulus of public concern and the interest of scientists and engineers in improving research practice so as to address that concern.
Computational Neuroscience and Compartmental Modeling
Published in Bahman Zohuri, Patrick J. McDaniel, Electrical Brain Stimulation for the Treatment of Neurological Disorders, 2019
Bahman Zohuri, Patrick J. McDaniel
Müller22 goes on to say that, there is also “Big Ethics” of artificial intelligence that asks about a very large impact on society, and on the humankind. A discussion of this issue is relatively new in academic circles. Stuart Russell has called it the question “What if we succeed” at IJCAI 2013.25
Promoting Ethical Deployment of Artificial Intelligence and Machine Learning in Healthcare
Published in The American Journal of Bioethics, 2022
Kayte Spector-Bagdady, Vasiliki Rahimzadeh, Kaitlyn Jaffe, Jonathan Moreno
The ethics of artificial intelligence (AI) and machine learning (ML) exemplify the conceptual struggle between applying familiar pathways of ethical analysis versus generating novel strategies. Melissa McCradden et al.’s “A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning” puts pressure on this tension while still attempting not to break it—trying to impute structure and epistemic consistency where it is currently lacking (McCradden et al. 2022). They highlight an “AI chasm” “generated by a clash between the…cultures of computer science and clinical science,” but argue that the “ethical norms of human subjects research” are still the right pathway to bridge this divide.
Rethinking the AI Chasm
Published in The American Journal of Bioethics, 2022
McCradden et al.’s (2022) article makes a distinctive contribution to the growing literature on the ethics of artificial intelligence in medicine. Not only do the authors raise important ethical issues that must be considered in this domain, but their analysis is augmented by the identification of root causes of ethical conflicts, considerations of how ethical issues arise at different timepoints in the process of developing these technologies, and instead of only ethical principles that relate to ML in health, they provide a specific and familiar governance pathway that can be followed.
Student-led peer review of an online teaching file: perspectives after 2 years
Published in Medical Education Online, 2021
Bryan R. Bozung, Kaiulani Houston, John F. Lilly, Sheryl G. Jordan, Lynn A. Fordham, Gary Beck Dallaghan
Editors ensured presentations generally followed a recommended format, providing consistency throughout the teaching file. Peer reviewed cases approved for publication were then assigned to a subspecialty category and uploaded to the online teaching file. An ‘Emerging Knowledge’ category included student presentations that departed from standard case files and addressed broader ideas such as ethics and artificial intelligence in radiology.