A parallelistic approach toward ontology design to overcome system's nuance in decision governance

Abstract

Artificial intelligence (AI) or machine intelligence (MI) can be faster and more accurate in domain specific decision tasks than human, however, AI's inability to achieve true general human creative cognitive capacity is still a deficiency in the AI or MI decision-making systems. The fundamental problem of decision-making governance, thus, poses a great challenge in advancing human-intelligent and machine-intelligent systems. The purpose of this paper is to present a noble parallelistic approach that has recently been applied to build a top-level ontology for HI-MI decision governance. The objective of this paper thus is threefold: 1) to address the gap in existing knowledge in decision governance for HI-MI systems and as a potential solution to the problem; 2) outline an integrative methodology to detailing the top to application level ontological development, resulting in; 3) a more specific 'parallelistic approach' for the development of a top ontology for human-machine intelligence decision governance.