The 4th industrial revolution is the confluence of the digital, physical and biological systems is changing the fields of engineering, finance, and economics. One of the digital technologies that are changing this landscape is artificial intelligence (AI). In recent years, AI decision-making and autonomous systems have become an integrated part of the economy, industry and society. The evolving economy of the human-AI ecosystem raises concerns regarding value bias and risks inherited by AI systems.
The availability and the increasing demand for AI systems created by many disciplines of intelligent systems addressing sub-aspects of the problem-solving process (e.g., different learning methods, data storage, information retrieval and more) create discrete components. These discrete components might individually be efficient and effective but as a combined system their effectiveness is reduced. Even though there is increased efficiency of each intelligent system, this does not necessarily lead to increased effectiveness of the overall system. To explain this in a framework, each subsystem can individually optimize but collectively be sub-optimal. For example, face recognition systems can be individually optimized for sub-populations but not be globally optimized because they are based on partial data.