Proceedings of the American Society for Engineering Management 2017 International Annual Conference
38th American Society for Engineering Management International Annual Conference, 18-21 October 2017, Huntsville, Alabama USA
The increasing CPU power and memory capacity of computers, and now computing appliances, in the 21st century has allowed accelerated integration of artificial intelligence (AI) into organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational processes including medical diagnosis, automated stock trading, integrated robotic production systems, telecommunications routing systems, and automobile fuzzy logic controllers. Self-driving automobiles are just the latest extension of AI. This thrust of AI into organizations and everyday life rests on the AI community’s unstated assumption that “…every aspect of human learning and intelligence could be so precisely described that it could be simulated in AI. With the exception of knowledge specific areas …, sixty years later the AI community is not close to coding global human intelligence into AI.” (Cotter, 2015). Thus, in complex mission-environment situations it is therefore still debatable whether and when human or machine decision capacity should govern or when a joint human-intelligence/machine-intelligence (HI-MI) decision capacity is required. Most important, there has been no research into the governance and management of human-intelligent/machine-intelligent decision processes. To address this gap, research has been initiated into an HIMI decision governance body of knowledge and discipline. This paper updates progress in one track of that research, specifically into establishing the ontological basis of HI-MI decision governance, which will form the theoretical foundation of a systemic HI-MI decision governance body of knowledge.
Original Publication Citation
Mahmud, F., & Cotter, T. S. (2017). Human-intelligence/machine-intelligence decision governance: An analysis from ontological point of view. In E-H. Ng, B. Nepal, & E. Schott (Eds.), Proceedings of the International Annual Conference of the American Society for Engineering Management. (pp. 1-8). American Society for Engineering Management (ASEM).
Mahmud, Faisal and Cotter, Teddy Steven, "Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis from Ontological Point of View" (2017). Engineering Management & Systems Engineering Faculty Publications. 126.