| Abstract |
Real-life AI based solutions usually consist of a complex chain of processing elements, which may include a mixture of machine learning based approaches and traditional programmed knowledge. The solution uses this chain of processing elements to convert input information into an output decision. When information is provided for a speci fic solution, the impact of the information on the decision can be measured quantitatively as a Value of Information (VoI) metric. In prior work, we have considered how the VoI metric can be de ned for a single AI-based processing element. To be useful in real-life solution instances, the VoI metric needs to be enhanced to handle a complex chain of processors, and be extended to AI-based solutions, as well as supporting elements that may not necessarily use AI. In this paper, we propose a de finition of VoI that can be used across AI-based processing, as well as non AI based processing, and show how the construct can be used to analyze and understand the impact of a piece of information on a chain of processing elements. |