Aware Adoption of AI: from Potential to Reusable Value

Artificial Intelligence (AI) provides practical advantages in different applied domains. This is changing the way decision-makers reason about complex systems. Indeed, broader visibility on greater information (re)sources, e.g. Big Data, is now available to intelligent agents. On the other hand, decisions are not always based on reusable, multi-purpose, and explainable knowledge. Therefore, it is necessary to define new models to describe and manage this new (re)source of uncertainty. This contribution aims to introduce a multidimensional framework to deal with the notion of Value in the AI context. In this model, Big Data represent a distinguished dimension (characteristic) of Value rather than an intrinsic property of Big Data. Great attention is paid to hidden dimensions of value, which may be linked to emerging innovation processes. The requirements to describe the framework are provided, and an associated mathematical structure is presented to deal with comparison, combination, and update of states of knowledge regarding Value. We introduce a notion of consistency of a state of knowledge to investigate the relation between Human and Artificial intelligences; this form of uncertainty is specified in analogy with two scenarios concerning decision-making and non-classical measurements. Finally, we propose future investigations aiming at the inclusion of this form of uncertainty in the assessment of impact, risks, and structural modelling.