Aware Adoption of Artificial Intelligence and Big Data: a Value
  Framework for Reusable Knowledge
  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 (BD), is now available to intelligent agents. On the other hand, such
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 formal framework to deal with the
notion of Value in the AI-BD context, embracing both the multiplicity of Value
dimensions and the uncertainty in their visibility as the foundations for a
dynamic, relational representation of Value. 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. In such a model, both Big Data and different types of
intelligence are considered as resources. The information extracted from data
becomes a renewable resource if it can be transformed into knowledge, which is
reusable beyond a specific scenario and, dynamically, over time.
  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 related to data observability. Finally, we identify applicative
domains for future investigation, in order to address the impact of the dynamic
behaviour of Value dimensions on strategies and decision-making, enhancing the
adaptability and, hence, the sustainability of AI-BD initiatives over time.
