=Paper= {{Paper |id=Vol-3311/paper8 |storemode=property |title=Formal Methods Meet XAI: the Tool DEGARI 2.0 for Social Inclusion |pdfUrl=https://ceur-ws.org/Vol-3311/paper8.pdf |volume=Vol-3311 |authors=Antonio Lieto,Gian Luca Pozzato,Manuel Striani,Stefano Zoia,Rossana Damiano |dblpUrl=https://dblp.org/rec/conf/aiia/LietoPSZD22 }} ==Formal Methods Meet XAI: the Tool DEGARI 2.0 for Social Inclusion== https://ceur-ws.org/Vol-3311/paper8.pdf

Formal Methods Meet XAI: the Tool DEGARI 2.0 for
Social Inclusion
Antonio Lieto1 , Gian Luca Pozzato1 , Manuel Striani1 , Stefano Zoia1 and
Rossana Damiano1
1
Dipartimento di Informatica, Università di Torino, Via Pessinetto 12, 10149, Italy

Abstract
We exploit the Description Logic TCL in order to develop a diversity-seeking affective recommender
system. The tool DEGARI 2.0 (Dynamic Emotion Generator And ReclassIfier) is an explainable, affective-
based, art recommender that allows to classify and to suggest, to museum users, cultural items able to
evoke not only the very same emotions of already experienced or preferred objects, but also novel items
sharing different emotional stances. The system has been tested on the community of deaf people and
on the collection of the GAM Museum of Turin, obtaining promising results.

Keywords
Description Logics, Nonmonotonic Reasoning, Explainable AI, Recommender Systems

1. Introduction
In this work, we present DEGARI 2.0, a Description Logic based recommender system, aimed
at bridging the differences in the experience of art between different communities, including
people with sensory impairments like the Deaf; the latter, indeed, represent the target group
of our system and of its evaluation. Our system, introduced in [1], aims at overcoming the
limitations of traditional recommendation approaches by exploiting a novel, publicly available,
ontological version of Plutchik’s model of emotions [2], equipped with opposition and similarity
relations between (basic and complex) emotions, as established in the Plutchik’s theory. To this
aim, we exploit the Description Logic TCL , a nonmonotonic extension of the typicality logic
𝒜ℒ𝒞 + TR introduced in [3] for tackling the problem of conceptual combination.
In practice, DEGARI 2.0 employs such ontological structure to suggest museum items not
only labeled with the same emotions, but - as mentioned - also to group and recommend
artworks evoking similar (but not exactly the same) emotions or opposite emotions. This kind
of alternation in the content suggestion mechanism aims at leading to more comprehensive
exploration and fruition of museum collections. Indeed, suggesting museum items evoking

OVERLAY 2022: 4th Workshop on Artificial Intelligence and Formal Verification, Logic, Automata, and Synthesis,
November 28, 2022, Udine, Italy
$ antonio.lieto@unito.it (A. Lieto); gianluca.pozzato@unito.it (G. L. Pozzato); manuel.striani@unito.it (M. Striani);
stefano.zoia@unito.it (S. Zoia); rossana.damiano@unito.it (R. Damiano)
https://www.antoniolieto.net (A. Lieto); http://www.di.unito.it/~pozzato/ (G. L. Pozzato);
http://www.di.unito.it/~rossana/ (R. Damiano)
0000-0002-8323-8764 (A. Lieto); 0000-0002-3952-4624 (G. L. Pozzato); 0000-0002-7600-576X (M. Striani);
0000-0002-5797-9542 (S. Zoia); 0000-0001-9866-2843 (R. Damiano)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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different emotions from the ones already experienced via the fruition of other artworks, is
based on the notion of perspective taking [4], i.e. seeing the world (e.g. an exhibition in this
case) from other perspectives. Since this approach is used to promote empathy, cohesion and
inclusion across social groups, reaching this goal would represent a huge advancement with
respect to the current technologies (e.g. like social media or standard recommender systems)
that often lead people toward content that fits their own viewpoint, promoting fragmentation
and fostering confirmation biases, instead of cohesion, inclusive reflection, and critical thinking.

2. The TCL Logic for Combining Prototypes
The core component of DEGARI 2.0 relies on a probabilistic extension of a Description Logic
called TCL (Typicality-based Compositional Logic), introduced in [5, 3]. This framework allows
one to describe and reason upon an ontology with commonsense (i.e. prototypical) descriptions
of concepts, as well as to dynamically generate novel prototypical concepts in a knowledge base
as the result of a human-like recombination of the existing ones [6].
The logic TCL is the result of the integration of two main features: (i) the extension of a
nonmonotonic Description Logic of typicality 𝒜ℒ𝒞 +TR , introduced in [7, 8], with a distributed
semantics based on the DISPONTE semantics of [9] and restricted to typicality inclusions; (ii) the
adoption of a well established heuristics inspired by cognitive semantics for concept combination
and generation [10] where, in order to formalize a dominance effect between the concepts to be
combined, for every combination we distinguish: a HEAD, representing the stronger element of
the combination (i.e. the one from which we want to inherit more properties in the final output
of the combination), and one or more MODIFIERS. In the logic TCL , typical properties can be
directly specified by means of a typicality operator T enriching the underlying Description
Logic, and a knowledge base can contain inclusions of the form 𝑝 :: T(𝐶) ⊑ 𝐷 to represent
that “typical 𝐶s are also 𝐷𝑠”, where 𝑝 is a real number between 0.5 and 1, representing the
probability of finding elements of 𝐶 being also 𝐷. The resulting knowledge base is a triple
⟨ℛ, 𝒯 , 𝒜⟩ where ℛ contains standard, rigid inclusions of the form 𝐶 ⊑ 𝐷 (all 𝐶s are also 𝐷s),
𝒯 contains typicality inclusions 𝑝 :: T(𝐶) ⊑ 𝐷 and 𝒜 is the ABox containing facts about
individuals, e.g. 𝐶(𝑎) (𝑎 is a member of concept 𝐶). From a semantic point of view, we consider
models equipped by a preference relation among domain elements as in [7], where 𝑥 < 𝑦 means
that 𝑥 is “more normal” than 𝑦, and that the typical members of a concept 𝐶 are the minimal
elements of 𝐶 with respect to this relation. An element 𝑥 is a typical instance of a given concept
𝐶 if 𝑥 belongs to the extension of the concept 𝐶, written 𝑥 ∈ 𝐶 ℐ , and there is no element in
𝐶 ℐ “more normal” than 𝑥. In order to perform useful nonmonotonic inferences, we consider
the stronger semantics introduced in [7], where entailment is restricted to a class of minimal
canonical models, intuitively those minimizing the atypical instances of concepts. The resulting
logic corresponds to a notion of rational closure built on the top of 𝒜ℒ𝒞 + TR .
The logic TCL extends 𝒜ℒ𝒞 + TR with the distribution semantics known as DISPONTE [9],
which is able to deal with probabilities equipping inclusions and allowing us to describe the
notion of scenario [3]: intuitively, a scenario is a knowledge base obtained by considering all
rigid properties in ℛ as well as all ABox facts in 𝒜, but only a subset of typicality properties in
𝒯 . The idea is to assume that each typicality inclusion is independent from each other in order
to define a probability distribution over scenarios: roughly speaking, a scenario is obtained by
choosing, for each typicality inclusion of 𝒯 , whether it is considered as true of false. Reasoning
can then be restricted to either all or some scenarios. We also equip each scenario with a
probability, easily obtained as the product, for each typicality inclusion, of the probability 𝑝 in
case the inclusion is involved, (1 − 𝑝) otherwise. It immediately follows that the probability
of a scenario introduces a probability distribution over scenarios, that is to say the sum of the
probabilities of all scenarios is 1.
In the logic TCL , in order to deal with the problem of combining prototypical descriptions of
concepts as in [3], we adopt typicality inclusions in order to formalize typical properties for both
the HEAD and the MODIFIERS concepts, and then to exploit the DISPONTE semantics in order
to select only some typical properties belonging to them characterizing the combined concept.
The preferential semantics underlying the logic TCL , together with the HEAD-MODIFIER
heuristics, are able to tackle the problem of conflicting properties.
Formally, given a knowledge base 𝒦 = ⟨ℛ, 𝒯 , 𝒜⟩ and given two concepts 𝐶𝐻 and 𝐶𝑀
occurring in 𝒦, our logic allows one to define the compound concept 𝐶 as the combination of
the HEAD 𝐶𝐻 and the MODIFIER 𝐶𝑀 , where 𝐶 ⊑ 𝐶𝐻 ⊓ 𝐶𝑀 and the typical properties of the
form T(𝐶) ⊑ 𝐷 to ascribe to the concept 𝐶 are obtained in the set of scenarios that: 1. are
consistent; 2. are not trivial, in the sense that the scenarios considering all typical properties of
the HEAD that can be consistently ascribed to 𝐶 are discarded; 3. are those giving preference
to the typical properties of the HEAD 𝐶𝐻 (with respect to those of the MODIFIER 𝐶𝑀 ) with
the highest probability. The set of scenarios remaining are those selected by the logic TCL as
the result of the procedure. The knowledge base obtained as the result of combining concepts
𝐶𝐻 and 𝐶𝑀 into the compound concept 𝐶 is called 𝐶-revised knowledge base:

𝒦𝐶 = ⟨ℛ, 𝒯 ∪ {𝑝 : T(𝐶) ⊑ 𝐷}, 𝒜⟩,

for all 𝐷 such that T(𝐶) ⊑ 𝐷 belongs to the selected scenario(s).

3. The tool DEGARI 2.0
DEGARI 2.0 exploits the logic TCL in order to provide an ontological formalization of the
circumplex theory of emotions devised by the cognitive psychologist Robert Plutchik [11],
[2]. According to this theory, emotions, and their interconnections, can be represented on
a wheel, in which the affective distance between different emotional states is a function of
their radial distance. The Plutchik’s ontology, formalizing such a theory, encodes emotional
categories in a taxonomy, representing: basic or primary emotions; complex (or compound)
emotions; opposition between emotions; similarity between emotions. In particular, by following
Plutchik’s account, complex emotion are considered as resulting from the composition of two
basic emotions (where the pair of basic emotions involved in the composition is called a dyad).
The compositions occurring between similar emotions (adjacent on the wheel) are called primary
dyads. Pairs of less similar emotions are called secondary dyads (if the radial distance between
them is 2) or tertiary dyads (if the distance is 3), while opposites cannot be combined.
The information about the emotional concepts and their corresponding features to combine
via TCL are extracted from the NRC Emotion Intensity Lexicon [12]: such lexicon provides a
list of English words, each with real-values representing intensity scores for the eight basic
emotions of Plutchik’s theory. The intensity scores were obtained via crowd-sourcing, using
best-worst scaling annotation scheme. This lexicon associates words to emotional concepts
in descending order of emotional intensity and, for our purposes, we considered the most
intensively associated terms for each basic emotion as typical features of such emotion. In this
way, the prototypes of the basic emotions were formed, and the TCL reasoning framework is
used to generate the compound emotions. Such prototypes of basic emotions are formalized by
means of a TCL knowledge base, whose TBox contains both rigid inclusions of the form

BasicEmotion ⊑ Concept,

in order to express essential desiderata but also constraints, as an example Joy ⊑ PositiveEmotion
as well as prototypical properties of the form

𝑝 :: T(BasicEmotion) ⊑ TypicalConcept,

representing typical concepts of a given emotion, where 𝑝 is a real number in the range (0.5, 1],
expressing the frequency of such a concept in items belonging to that emotion: for instance,
0.72 :: T(Surprise) ⊑ Delight is used to express that the typical feature of being surprised
contains/refers to the emotional concept Delight with a probability/degree of belief of the 72%.
Once the association of lexical features to the emotional concepts in the Plutchik’s ontology
is obtained and the compound emotions are generated via the logic TCL , the system is able
to reclassify the cultural items in the novel formed emotional categories. Intuitively, an item
belongs to the new generated emotion if its metadata (name, description, title) contain all the
rigid properties as well as at least the 30% of the typical properties of such a derived emotion.
The 30% threshold was empirically determined: i.e., it is the percentage that provides the better
trade-off between over-categorization and missed categorizations [13, 14].

4. Conclusions
We have tested DEGARI 2.0 with members of Istituto dei Sordi and on the collection of the
GAM Museum of Turin. The experiments provided in [1] show that the effort of tackling
diversity-seeking, affective-based and explainable museum recommendations received a moder-
ate, improvable, acceptance from the deaf community. This is an encouraging result considering
the challenge of the cognitive barriers involved in the process of the accepting suggestions that
do not fit one’s own preferences and viewpoints.
Experiments concerning the perceived explainability of the provided categorization lead to
some key elements emerged as guidelines to design and improve the next generation of inclusive
and transparent AI systems, potentially going beyond the specific needs of the deaf community.
In this regard, it is important to point out how state of the art neural systems and language
models, like SenticNet 7, do not have, as a built-in, this feature. It represents, however, one of
the major requirements for modern AI systems interacting with the humans (see the recent
General Data Protection Regulation (GDPR) that emphasized the users’ right to explanation
[15]).
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