Aware Adoption of Artificial Intelligence and Big Data: a Value Framework for Reusable Knowledge

Abstract

Artificial Intelligence (AI) is changing the way decision-makers reason about complex systems: more information (re)sources, e.g. Big Data (BD), are now available, but decisions are not always based on reusable and explainable knowledge resulting from the direct interaction with data. Therefore, it is necessary to define new models to describe and manage this type of uncertainty. This contribution introduces a conceptual framework to deal with the notion of Value in AI-BD contexts, embracing both the multiplicity of Value dimensions and the uncertainty in their visibility as the foundations for a dynamic, relational representation of Value. The purpose is to provide ad hoc models to support Business Intelligence in assessing the impact of AI-BD projects. The framework design is based on abstract and highly scalable definitions to represent Value, even considering the interaction of different agents through comparison, combination, and update of states of knowledge. The focus on reusable knowledge is exploited in the relation between Human and Artificial intelligences, which is characterised by a non-classical form of uncertainty regarding data observability. The impact of the dynamic behaviour of Value dimensions on decision-making and potential application domains are discussed, with the aim to enhance the sustainability of AI-BD initiatives over time.Comment: 32 pages, 5 figures. Improved exposition, corrected typos. Comments are welcome