This is a preview of subscription content, log in via an institution to check access.
References
Perrow C. Normal Accidents: Living with High-Risk Technologies. Princeton: Princeton University Press, 1984
Weick K E, Sutcliffe K M, Obstfeld D. Organizing for high reliability: processes of collective mindfulness. Research in Organizational Behavior, 1999, 21: 23–81
Scharre P. Army of None: Autonomous Weapons and The Future of War. New York: W. W. Norton & Company, 2018
Chow Y, Tamar A, Mannor S, Pavone M. Risk-sensitive and robust decision-making: a CVaR optimization approach. In: Proceedings of the 28th International Conference on Neural Information Processing Systems. 2015, 1522–1530
Bresina J L, Morris P H. Mixed-initiative planning in space mission operations. AI Magazine, 2007, 28(2): 75–88
SouthWales Police. https://doi.org/www.south-wales.police.uk/en/advice/facialrecognition-technology/ Accessed November 12, 2018
Author information
Authors and Affiliations
Corresponding author
Additional information
Thomas G. Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor Emeritus in the School of Electrical Engineering and Computer Science at Oregon State University, USA where he joined the faculty in 1985. Dietterich is one of the pioneers of the field of Machine Learning and has authored more than 190 refereed publications and two books. His research is motivated by challenging real world problems with a special focus on ecological science, ecosystem management, and sustainable development. He is best known for his work on ensemble methods in machine learning including the development of error-correcting output coding. Dietterich has also invented important reinforcement learning algorithms including the MAXQ method for hierarchical reinforcement learning.
Dietterich has devoted many years of service to the research community. He is Past President of the Association for the Advancement of Artificial Intelligence, and he previously served as the founding president of the International Machine Learning Society. Other major roles include Executive Editor of the journal Machine Learning, co-founder of the Journal for Machine Learning Research, and program chair of AAAI 1990 and NIPS 2000. He currently serves as moderator for cs. LG, the machine learning category on ArXiv. Dietterich is a Fellow of the ACM, AAAI, and AAAS.
Rights and permissions
About this article
Cite this article
Dietterich, T.G. Robust artificial intelligence and robust human organizations. Front. Comput. Sci. 13, 1–3 (2019). https://doi.org/10.1007/s11704-018-8900-4
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11704-018-8900-4