AIEs are both hardware and software entities that perform tasks in ways that are “intelligent”:4
indeed, they are not just programmed for a single and repetitive motion but they can adapt to do more
(and in a better way) by adapting to different situations and contexts. Nevertheless, they are able to
understand languages, recognize pictures, solve complex problems by themselves and learn5 as they
go along6 without constant supervision (e.g. machine learning7). Their decision-making process is
usually based on symbolic reasoning, analyses of the user’s behaviour, experience, data acquisition
and it is characterized by a heuristic8 approach.9 Differently from the human mind which depends on
the Johnson-Laird’s mental model theory10 and often falls into biases, AIEs can not only remove the
deductive fallacies in reasoning but they can increase the decision-making process through the
conditional probability (Bayes’ theorem). Furthermore, they are able to set up an inductive reasoning
reducing both decisional conflicts and cognitive dissonances.
4 Besides th e different attempts to define intelligence across psychological branches (cognitive, behaviourist, dynamic,
Piagetian), as well as its intuitive conceptualization by the common sense ,“… if we attempt to dig deeper and define it
in precise terms we find the concept to be very difficult to nail down… Intelligence involves a perplexing mixture of
concepts, many of which are equally difficult to define.” Shane Legg and Marcus Hutter, ‘Universal Intelligence: A
Definition of Machine Intelligence’ (2007) 17 Minds and Machines 391. An interesting approach to intelligen ce which
allow to get closer to AI structure (especially neural networks) is the “symbol system” approach, that is “…the ability of
human beings to use various symbolic vehicles in expressing and communicating meanings distinguishing human beings
sharply from other organisms.”- see Howard Gardner, F rames of Mind: The Theory of Multiple Inte lligences (3rd edn,
Basic Books 2011), 26.
5 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (3rd edn, Pearson, 2016), 1 -5, 36-40, 64-
6 Namely, they “…will collect info rmation without an express instruction to do so, select information from the universe
of available data without direction, make calculations without being told to do so, make recommendations without being
asked and implement decisions without f urther authorization… [they] will truly execute their decisions with real data in
a complex networked environment, and will af fect real world events”- Curtis E.A. Karnow, ‘Liability for Distributed
Artificial Intelligence’ (1996) 11(1) Berkeley Technology and Law Journal 147, 152-153.
7 Especially this type of AIEs are dissimilar from traditional analytics: indeed, they modify the underlying constitutive
algorithm according to data which they have previously processed. As output, they learn new schem es of information.
8 A complex and innovative mix of “strategies using readily accessible information to control problem-solving processes
in man and machine” by using approximate algorithms- Judea Pearl, Heuristics: Intelligent Search Strateg ies for
Computer P roblem Solving (Addison-Wesley, 1984) 3. Regarding the AI context, heuristic is a function that (based on
trade-off criteria such as optimality, completeness, accuracy and time) ranks alternatives in search algorithms at each
branching step based on available information to decide which branch to follow. In these terms, the “heuristic search
remains as a core area of artificial intelligence. The use of a good search algorithm is often a critical factor in the
performance of an intelligent system. As with most areas of AI, there has been steady progress in heuristic search research
over the years. This progress can be measured by several different yardsticks, including ﬁnding optimal solutions to larger
problems, making higher quality decisions in fixed size p roblems, handling more complex domain s including dynamic
environments with incomplete and uncertain information, being able to analyze and predict the p erformance of heuristic
search alg orithms, and the increasing deployment of real-world applications of search algorithms”- Weixiong Zhang,
Rina Dechter and Richard E. Korf, ‘Heuristic Search in Artificial Intelligence’ (20 01) 129 Artificial Intelligence 1.
9 Fabio Bravo, Contra ttazione Telematica e Contra ttazione Cibernetica (Giuffrè Editore , 2007), 196-209.
10 Philip Johnson-Laird, ‘Mental models and human reasoning.’ (2010) 107(43) P roceedings of the National Academy of