<table><caption>Table 7.3: Exploratory narrative questions to interpret a visual work of art</caption><thead><tr><th>Question</th><th>Sub-questions</th></tr></thead><tbody><tr><td>?Who</td><td>What is the artist's name?<br>What is the artist's movement?<br>When did he live?<br>Where did he live?<br>What other artworks did he do?</td></tr><tr><td>?What</td><td>What is the type of the artwork?<br>What is the genre of the artwork?<br>What is the material of the artwork?<br>What is the subject of the artwork?<br>What is the source of the artwork?<br>Are there symbolic meanings?</td></tr><tr><td>?Where</td><td>Where was the artifact created?</td></tr><tr><td>?When</td><td>Which is the exact date of creation of the artwork?<br>In what cultural tradition or era was it made?<br>Are there other historical and cultural events that are possibly related to the artwork?</td></tr><tr><td>?Why</td><td>Who commissioned the artwork?</td></tr></tbody></table>

was added to the graph as an INN Artwork node, which constituted the starting point of the information retrieval. The 5W narrative questions and their sub-questions were added<fnref n="16" />. Figure 7.4 illustrates how the network visualisation appears at this stage. The following sections illustrate in detail the network expansion according to each main question, as information is retrieved from the cited KBs.

**?Who** This question concerned the author since no information about other involved people (.e.g the patron) was available on the KBs. It was possible to retrieve the author's date of birth and death, places and art movement. Since this information partially answered other open narrative questions, such as **?When** and **?Art movement** (sub-question of **?What**), a relation between them was added. It was also possible to retrieve other paintings by the same author, which is relevant information for art historical research, e.g. for style comparison, or analysing if and how the painter depicted similar subjects.

**?What** This question considered the retrieval of metadata, such as the title, type, genre, material and art movement of the painting. All the sub-questions found an answer on Wikidata and Zeri. We then answered the narrative question **?Subject**, sub-question in the **?What** variable, which retrieved a great amount of information. In Wikidata, two ways for registering subjects are available, namely specific objects (**wdt:P180** 'depicts') or the main subject (**wdt:P921** 'main subject'). The graph is enriched with the subjects depicted (**wdt:P180** 'depicts'). Whereas Zeri&Lode included only one entity for the subject ('Venere e Cupido'), related to the Wikidata entity thanks to their common alignment to the Iconclass code 92C454, the Iconology dataset provided a thorough description. The identified subjects are here described according to three layers of understanding, showing how more concrete objects (e.g. 'woman') relate to iconographies (e.g. 'Venus') and to deeper meanings (e.g. 'wedding wish'). For answering this

<footnote n="16">¹⁶The visualisation of the graph at each stage is available at https://inn-painting-art-interpretation.streamlit.app/Network_Evolution</footnote>
<figure><img src="image_35.png" /><figcaption>Figure 7.4: How the network appears (a) with the 5WH questions and sub-questions still unbound, (b) after answering the ?Who question, (c) the ?What question, and (d) at the end of the information retrieval.</figcaption></figure>
question, also the results obtained by CV analysis were integrated, despite only the concept of
'person' was recognized, and the algorithm erroneously recognized Venus's diadem ad a hat.
Following, the sources of the depicted theme were retrieved. In detail, the painting is based on
an epithalamium, i.e., a classical genre of poems written to celebrate weddings (source: Wiki-
data), and various objects and their meanings have specific sources, e.g. the incense burner as
a proper bridal chamber decoration has as source the epithalamium of the roman poet Sidonius
(source: Iconology Dataset).

**?When** As anticipated, the retrieval of information about the artist partially answered the question, restricting the range of the possible date of the artwork creation to the one of the author's life. The information about a more precise date of creation was provided by Wikidata (inception 1530) and Zeri (1520-1556).

**?Where** For this section, the main questions raised for a better understanding of the artwork are 1) the location where the artwork was created, and 2) the place where the artwork was intended to be displayed. Whereas the first question can be potentially answered through the modelling adopted by Zeri, the second one can be retrieved on Wikidata. Nevertheless, no further information was available for Lotto's painting. Therefore, the question is partially answered by the information about the author's work location previously retrieved in the ?Who question. We consequently added a relation between the narrative questions ?Location and ?Where

**?Why** The narrative question ?Why had as a main sub-question the retrieval of the patron, namely the person who commissioned the artwork. Although this sub-question can be formally expressed with Wikidata ontology with the relation wdt:P88, such information is not available for the chosen case study. Therefore, the narrative question remains unbound.

**Second iteration** After the first binding of the main information to the narrative questions, we performed a second iteration for answering potential questions raised by the user. For example, a user may be interested in continuing the exploration of a certain KB starting from a retrieved node, or retrieving more possible meanings through the exploration of new KBs. For example, we retrieved more information about Venus, Cupid and their Greek counterparts (Aphrodite and Eros), discovering that they are gods of love and that the latter is the son of the former. Secondly, we explored the potentially embedded symbolism not yet discovered by querying HyperReal. As a result, we included in the INN representation potential symbolical meanings of Venus, the tree and the cone shell, finding, for example, that the conch is not only one of the attributes of Venus but is also a symbol of fertility and gestation, concepts supporting the meaning of the artwork as a wedding wish.

By implementing the questions raised by the literature, the major part of the questions could be expressed as SPARQL queries, performed and answered with currently available information on knowledge graphs. Some answers were bound to multiple questions, as they answered both. Despite multiple information could be aligned through the strategies of reconciliation with a common vocabulary, word similarity, and synonyms detection, challenges in information integration remain open (e.g. when pieces of information slightly differ, as happened with the date of creation). As a solution, we make the INN available as an interactive tool as described in the following section, in which the user can manually select the desired data and perform reconciliations.
<figure><img src="image_36.png" /><figcaption>Figure 7.5: This Figure shows an interface for art historians to steer the INN expansion process. The historian can click on certain questions to expand them further and zoom in and out.</figcaption></figure>

## 7.4 Utilisation of the INN by art historians

The INN is a central data structure around which various applications can be developed. One application, which we have operationalized, is intended for art historians. It allows the historian to direct the expansion of the INN by indicating which questions should be preferentially explored and by choosing which knowledge sources should be utilised preferentially.

Figure 7.5 shows a snapshot of this interface, which is linked to Wikidata. The user starts by searching for an artwork based on its Wikidata ID number; or alternatively, its creator or title. Once an artwork is selected, it becomes the initial node from which the five major WH-questions spring out: ?Who, ?What, ?Where, ?When, and ?Why. Based on this initial network, the art historian can develop a narrative that is supported by online knowledge sources by introducing and answering new narrative questions until a satisfactory ending (narrative closure) is reached. By giving the human user full control over the network, other potential issues – such as conflicting or erroneous information – can be solved manually.

Future implementations of this first prototype will consider the inclusion of feedback *to* the knowledge sources. For instance, a specific piece of information about the artwork may be missing. If the user adds a node for that information to their network, it should also be possible to transmit that information to collaborative graphs (e.g. Wikidata) and expand or update that knowledge source. In this way, while users develop their private network, they can simultaneously foster the improvement of collective data quality and the enrichment of knowledge graphs with their expert knowledge.

## 7.5 Conclusions

This paper introduced an application of Integrative Narrative Networks (INN), a data structure for representing progress in understanding, to the interpretation of artworks in the form of a graph that connects questions and answers for a specific work. Narrative closure occurs when the main questions of relevance to the human interpreter have been answered. The paper
defined the INN and focused on how NLP tools and semantic web resources about art history
and interpretation, which are becoming more and more available, can be marshalled to push
the interpretation forward. We used a case study of a Late Renaissance painting by Lorenzo
Lotto to illustrate the proposed methods and techniques to represent the progressive under-
standing that can be obtained through 1) the painting's catalogue entry processed through an
NLP tool and related to further knowledge available on Wikidata and 2) by retrieving and
integrating explorative information about the painting from multiple knowledge bases. Future
works include further refinements of the experiments. In particular, we foresee further testing
of the capabilities of other NLP algorithms to retrieve further entities and relations from the
catalogue entry, and to understand how semantic web sources can be helpful in establishing
relations among the extracted entities. Furthermore, we are planning to integrate additional
resources, conduct more case studies, expand the capabilities of the user interface for art his-
torians, develop another interface for viewers embedded in an augmented reality device so that
viewers can interactively explore an artwork in situ and explore gradually how an 'artificial
interpreter' can be built that semi-autonomously expands the Integrative Narrative Network.
Chapter 7: References

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# Chapter 8

## Understanding Artistic Creativity
through the Lens of AI

Luc Steels<fnref n="1" />

† Venice International University steels@arti.vub.ac.be

### Abstract

The paper contributes to the computational study of creativity using figurative painting as application domain. The proposed approach relies on a wide range of AI techniques (pattern recognition, computer vision, natural language processing, semantic web technologies) in order to reconstruct the interpretation of an art work and to investigate how the painter goes from a chosen topic and source image to a painting. The artist is seen as a ‘cognitive engineer’ who has deep intuitions about human visual perception, memory, and human psychology and exploits these insights to trigger a rich process of narrative construction in the viewer. The paper is empirically grounded in the artistic methods used by the contemporary painter Luc Tuymans and uses one of his paintings (Secrets) as a concrete case study.

Keywords:AI, Art, painting, knowledge processing, narrative construction

### 8.1 Introduction

#### 8.1.1 Approaches to computational creativity

Art is often associated with creativity and the study of art is seen as an effective way to study the creative process. The most common approach to study creativity uses methods from psychology to understand the personality traits and cognitive processes through observation and introspection [3]. An alternative approach consists of making computational models of art creation, a field now called computational creativity, with practical applications in terms of computational tools to support the creative process of artists, helping viewers interpret and experience art works, supporting art historians and curators to study and archive artistic methods, and improving art education [7].

Computational creativity research has been going on since the earliest days of computing and has meanwhile produced a large amount of theories, software, and even art works. The earliest

<footnote n="1">¹Supported by the EU Pathfinder project MUHAI (EU grant 951846)</footnote>
experiments were in the domain of music by Lejaren Hiller and Leonard Isaacson who presented in 1957 the first musical composition constructed by an AI system, called *Illiac Suite* [10]. Hiller and Isaacson pioneered a computational model of creativity, which is still predominantly used today. It is based on three components (i) a set of templates, (ii) a generator, which instantiates templates by making random choices for the open parameters but bounded by the constraints provided by a template, and (iii) a tester which evaluates the completeness and quality of instantiations. The music this process produced was certainly intriguing and an adequate performance could add emotional value making it easier (or in some cases harder) for listeners to build some sort of interpretive experience<fnref n="2" />

*Illiac Suite* sounded music-like and was playable by human violin players because Hiller and Isaacson were trained musicians who knew about the constraints of harmony and the common structures of Western music. So they could build a library of plausible templates, guide the generation within interesting boundaries and introduce tests that made musical sense. They experimented with Markovian decision processes to generate melodies and harmonic progressions, used common tempo's (presto, andante, allegro), choose familiar instruments, etc. Certainly not everybody liked this music and the whole idea of computational musical creativity was attacked vigorously. Whether music is enjoyable does not only depend on the composer and the performers but also the listeners who are invited to project structures and meanings on what they are hearing and may not be as adventurous as the composers. A lot of music is outside the psychological scope and background knowledge of the audience and gets forgotten in the mist of history.

In subsequent decades we have seen increased sophistication for the three steps in the Hiller and Isaacson model as applied to music and advances in computational techniques for constraint propagation and heuristic management of explosive search. The state of the art in computer music today is simply astounding (see the recent overview in [15]). These days you can actually play together with an artificial musician in real time. Not just from a common score but, as in a Jazz ensemble, with other players occasionally improvising. The computer has to 'listen' and analyze in real time what the other musicians are doing and creatively introduce improvisations within the melodic and harmonic framework of a Jazz Standard [17].

Very similar developments have taken place in other domains of art. We have seen steady progress in the domain of story generation, starting with the work by Schank and colleagues in the nineteen seventies [17] to more recent work, such as [21] or [9], using the same model of instantiating given templates either by adding specifics of a particular case (for example automatically generating weather reports based on weather data) or by random choices on how archetypal characters are filled in and actions chosen. Similarly for the visual arts, there have been remarkable advances in computer graphics which have given extraordinary tools for drawing and coloring, and computer-based support for computer animation [23]. There have also been important efforts to develop common standards for describing art works and its cultural context and building up archives for works in museum collections [4].

More recently, there have been remarkable developments in computational creativity, giving rise to a *data-driven approach*, in line with the wave of data-driven AI which has been rising based on significant advances in neural network learning (particularly deep learning and convolutional networks) and probabilistic learning, as well as tremendous increases in computer power and data to learn from. The templates have been replaced or complemented with statistically learned neural network models with remarkable results. For example, a network is trained with a large number of Bach chorals and then asked to produce a new variation. The results are remarkable in the sense that they are difficult to distinguish from the originals, particularly

<footnote n="2">²https://www.youtube.com/watch?v=n0njBFLQSk8</footnote>
if you are an amateur listener, although an expert listener will experience a lack of coherence and the feeling that snippets of musical phrases have been pasted together without an overall coherence. Similar developments have been happening in the domain of text generation [8]. Also in the domain of visual arts there has been a lot of excitement recently with the deployment of machine learning algorithms, in particular convolutional, deep neural networks and generative adversarial networks, in order to capture the style of an artist and produce works 'in the style of', for example, train networks with all the paintings of Claude Monet provide a picture of a natural scene and then see it transformed into the same style as captured in the statistical model [20]. There is an uncanny similarity of these artificial images with Claude Monet's paintings.

In this paper I explore an alternative path to the study of creativity and the design of experi-
ments in computational creativity, namely a *functional approach*. The functional approach does
not deny that authors, composers and painters know a large number of ready-made templates
and occasionally use a generate-and-test strategy, nor that many of these templates have been
acquired by hearing a lot of music, seeing a lot of paintings or reading a lot of texts. But it
claims there is something more going on. The key element missing is a concern with motivation
and meaning. Yes, the statistical models capture the style of a painting, a text or a musical
composition but they do so without taking into account the other levels of meaning that are
just as crucial in the creation and interpretation of art works.

For example, when a data-driven AI system constructs a painting in the style of Vincent van
Gogh based on a photograph of a face, it has no clue that it is dealing with a face. It has
no idea that the way van Gogh made his famous self-portrait reflects his tormented affective
states. The generated painting looks like a painting by van Gogh but does not capture the
meanings motivating his paintings. Similarly, if a data-driven system analyses or composes a
chorale in the style of Bach, it does not take into account the underlying story, the emotions
being expressed, the spiritual significance, or the role of chorals in fostering the coherence of
a community. It has derived a statistical model and pastes together short fragments that are
similar to fragments found in Bach's works, so that the result sounds like Bach. But it does
not entail the same depth of experience. The earlier template-based approach is in this sense
not much better. The templates are given by human design but which template is chosen and
how it is instantiated is not motivated.

From a functional point of view, creativity is happening when an (artistic) problem is solved
in a novel way. The artist is viewed as a cognitive engineer or designer, who has goals and
motivations and has to transform them into an artifact that works, in the sense of having the
intended effect on viewers or listeners [5]. Like in architecture or computer programming, a lot
of constraints have to be combined to make this happen. The artist has sophisticated problem
solving strategies, often obtained after years of training, practice and discovery, but also meta-
level strategies to invent new approaches to solving problems. Whereas the application of
known strategies already requires creativity and a lot of experience because a strategy will very
often not quite fit with the specific goals and circumstances of an art work, a more powerful
form of creativity occurs if the artist invents new strategies that either introduce new ways to
express meaning or enable the expression of new meanings.

8.1.2 Goals of the present paper

If a functional viewpoint is adopted, the aspiration to support or produce art by computers
becomes much more challenging and is certainly way beyond the current state of the art, not
so much for technical reasons but rather because we do not understand enough about artistic
methods from a functional perspective. In earlier papers I have discussed in general terms how
the functional approach might be applied to language [22] and music [21]. In this paper I focus
on the visual arts.

The main goal of this paper is to show a very first step by practicing a kind of ‘reverse engineering’, i.e. try to reconstruct how a viewer builds up meanings by looking at a painting and bringing to bear background knowledge from semantic and episodic memory. The next step will be to reconstruct how an artist starts from motivations and meanings and turns them into a painting by externalizing these meanings using artistic methods, i.e. using choices for colors, lines, modifications in recognizable figures, etc. This will help us to understand how the artist tries to trigger and influence the interpretative processes of the viewer. Only then we can identify at which points creative moves have been made by an artist or by viewers who try to make sense of these innovations.

To make the discussion concrete, I will report on a concrete case study, using a combination of AI algorithms drawn from diverse areas of AI (pattern recognition, computer vision, natural language processing, semantic web technologies) augmented with hand-coded operations to fill in gaps that we cannot yet fill. I focus in this paper more specifically on the paintings by a contemporary Flemish painter Luc Tuymans. Luc Tuymans is considered to be one of the most important contemporary painters at the moment [13], pioneering a renaissance of figurative painting. He has done solo exhibitions at some of the most prestigious and influential art centres in the world such as the MOMA in New York, the MCA Museum of Contemporary Art in Chicago, the Städel Museum in Frankfurt, the National Art Museum of Beijing, etc. Luc Tuymans is very articulate in describing his own artistic method as well as analyzing the methods used by other painters [24] and this has been crucial for developing the theoretical underpinning and the practical realization of the experiments reported here. Working with a living artist makes it possible to get access to archives and high resolution digital images of art works, obtain direct feedback about the relevance and utility of our approach, and get invaluable information about the artistic methods the painter is using or claims to be using.

This paper discusses only a single painting as shown at a recent solo-exhibition by Luc Tuymans at the Palazzo Grassi in Venice in 2019/2020. The painting is entitled ‘Secrets’. An image of the painting is shown in Figure 8.1, left. Through a web-based search process and using algorithms for matching and aligning paintings and their source (described in [1]), we have identified the original source for this painting. It was confirmed by the artist and shown in Figure 8.1, middle. This paper does not describe all the experiments we have performed on this painting or on the other paintings in the Palazzo Grassi exhibition. The reader is referred to [?], [1], and [21] for more technical details. An exhibition on the research discussed here has been shown at the BOZAR art center in Brussels in April 2021.

<figure><img src="image_37.png" /><figcaption>Figure 8.1: Left: The painting ‘Secrets’ by Luc Tuymans. Middle: The source image of the painting. Right: Regions based on K-means clustering with K=5. This clustering highlights some of the expressive devices the painter used to emphasize the character of the represented figure and the theme of hiding secrets.</figcaption></figure>
## 8.2 Motivations, meanings and narratives

There are many motivations for creating art works: to tell an exciting story, foster group feelings, spiritual exaltation, relaying information, reliving an experience (pleasant or dreadful), argueing for a particular moral or political stance. To achieve any of these goals, art works evoke a set of meanings in the viewer. The nature of meaning is difficult to pin down. It involves ways of categorizing real or imagined situations and of using these categories to construct *narratives*. Narratives contain objects, actors, events, roles of objects and actors in events, goals and motivations, temporal and causal relations, viewpoints from which a situation is perceived, a moral and political stance, an emotional position. Many of these meanings are subconscious and hard to express in language and the great power of art is that it can nevertheless evoke them.

Meanings and narratives are situated at different levels or planes. The art literature commonly uses the following levels - as proposed by Ervin Panofsky [16] for the visual arts and Aaron Copland [2] for music.

1. **Sensory level:** The sensory level relates to the raw sensory experiences of an art work, which can already give a great deal of pleasure. For example, when listening to a violin concerto in the background, one can already enjoy the warmth of the sound, when having a painting in the room it can already introduce an agreeable atmosphere purely based on the colors, textures and materials.

2. **Motif level:** Motifs are the first level up in which sensory experiences are categorized and organised. For painting we find at this level categories and structures related to lines or color regions which are aggregated into geometric figures or surfaces and shapes. The motifs in music refer to musical phrases, melodies, rhythmic structures, harmonic progressions.

3. **Figurative level:** This level describes a work in terms of the figures, situations, and events that have been depicted in figurative painting or figurative music. For example, looking at Claude Monet's *Impression, soleil levant* (impression, sun rise) (1872) we recognize a few boats on the sea, an orange sun above the water and some silhouettes of port structures. Listening to Claude Debussy's *De l'aube à midi sur la mer* (From sunrise to noon on the sea) (1903) we hear the invocation of the rising sun on the sea.

4. **Expressive level** The expressive level is about the affective states (emotions, moods) of the situations and persons being depicted or aroused in the viewer. For example, the famous aria *Va pensiero* from Giuseppe Verdi's opera Nabucco (1842) evokes nationalist pride against an oppressor and causes the audience to rise up and sing along until this day.

5. **Cultural level:** This level is about the historical, political and cultural background and the conventions that are behind the figures and emotions evoked by an art work without which its significance cannot be understood. For example, the painting *El tres de mayo de 1808* (the Third of May 1808) by Francisco Goya is a powerful depiction of Spanish resistance against the Napoleonic army. Its powerful emotional depiction of the horror's of war still feel like a deep cry against oppression today.

6. **Intrinsic motivation:** This level refers to the intrinsic motivation, originally the motivation of the artist, but art works can be the receptacle for new motivations as cultural conditions change or the images are appropriated for other purposes.

All these levels interact with each other in a single work and some works put more emphasis
on certain levels than others. The big challenge for an artist is to combine considerations at all
these levels into a single coherent whole, much like an architect needs to combine constraints
coming from material properties, functional desiderata, esthetics, building utilities, government
regulations, etc. There are often narratives at several of these levels. For example, a musical
work can tell a story using motives that are introduced, repeated, modified, and transformed
[14], as the examples of Bach's Wolhtemperierte Klavier or Jazz improvisations based on a
known Jazz standard show. The same happens in the abstract paintings of Mondriaan or
Kandinsky. There is no point to look for a figurative level. Narratives are obvious when
considering the figurative level. Now the motifs have become signifiers with a significance
beyond the level of form.

8.3 Integrative Narrative Networks

In order to carry out computational experiments or build computational tools we need a central data structure that describes the art work and its background from all these different level. I have proposed to call this data structure a **Integrative Narrative Network** or INN. It progressively expands as the interpretation or creation process unfolds [? ]. This data structure collects all information that is selectively considered to be *relevant* to the art work or situation the viewer is trying to understand. The INN does not necessary represent a single narration, i.e. a particular story or painting, but it captures the underlying narrative structures, the relevant background knowledge, and the intermediary steps in the visual, auditory or textual analysis of an artwork.

Technically speaking an INN is a weighted directed graph. The nodes in the graph are of three types: (i) *Individual concepts*, i.e. concepts which refer to individual entities that can be persons, physical objects, buildings, images, sounds, events, actions, etc. (ii) *Abstract concepts*, such as categories and classes, like types and properties of persons, causal and temporal relations, roles of entities in events, etc. and (iii) *Anchors* that ground individual or abstract concepts into physical reality as experienced through a sensory-motor embodiment, e.g. a region in an image, or into the semantic web through open linked data and URIs (Uniform Resource identifiers).

The links between the nodes are relations, weighted on the basis of their associative strength or certainty: *Semantic relations* reflect factual knowledge, similar to what is used in knowledge graphs. *Associative relations* suggest related concepts, for example, that tight lips may indicate anger. And *constructive relations* indicate which algorithm has established the relation between a concept and an actual image, visual scene, sound source or text fragment. Edge detection, color clustering or pattern recognition are examples of constructive relations. An INN quickly gets quite large, having thousands of nodes and links, even for a single image and one or two sentences as input. We use tools currently available for presenting and browsing through very large graphs to visualize an INN and its step-wise expansion, more specifically we use Gephi (https://gephi.org/). See the example in Figure 8.2

8.4 Expanding an INN

This section gives some examples on how Integrative narrative networks get created, modeling either the artist when researching a painting before actually producing it, or a viewer looking at the painting and interpreting it. The painting 'Secrets' is used as example. Due to space limitations only a few examples at some of the levels can be discussed briefly.
<figure><img src="image_38.png" /><figcaption>Figure 8.2: Small fragment of an INN built up for the painting Secrets. It is the area of the graph that contains background knowledge and associations triggered by the title of the exhibition 'La Pelle', which refers to a film and a book by Curzio Malaparte about the moral decay during and after World War II.</figcaption></figure>

### 8.4.1 Sensory processing and motif detection

Computer vision research has enormously advanced over the past decades and there are now thousands of algorithms that extract information from images. There are only a few dozen or so that we have tried on the painting Secrets (see more details in [? ] and [1]). Example results are shown in Figure 8.3.

1. **Focal regions:** Every image has certain regions or points to which the human eyegaze is naturally attracted. Painters exploit this phenomenon by deliberately creating focal regions in a painting which then act as potential signifiers. Figure 8.3(i) shows the result of the MSI algorithm [12], which is based on a neural network trained with data of human eye gazing when looking at natural images. Figure 8.3(ii) shows the result of another focal algorithm known as the spectral-residual approach [11]. The algorithm computes statistically significant points without any prior data of human vision. It gives a quite different result, tuned to significant edges rather than color contrast.

2. **Color clustering:** Color clustering algorithms, such K-means clustering where K refers to the number of expected clusters, is a way to group the pixels in an image into a set of classes. Once these classes are detected, each pixel in an image can be replaced by the central point of the cluster to which it belongs. This provides a way to 'zoom out' of color detail to reveal color regions that may be potential signifiers. An example is shown in Figure 8.1 right which shows this operation with K=5. The expessive significance of these regions is discussed shortly.

3. **Edge detection:** The detection and aggregation of edges into larger edges or surfaces is another intensely studied area of computer vision research, even though many algorithms appear not to work very well with paintings. Figure 8.3(iii) shows an example of edge detection on 'Secrets' (from [1]) using a neural network based algorithm called TIN recently introduced by Wibisono and Hang [25]. Once these edges are detected it is also possible to compare them to the result of the same algorithm on the original source image in order to bring out which edges are the same - meaning that the painter considered them essential to maintain the recognizability of the original - and which ones are different - suggesting that the painter wanted to transform the original by changing or highlighting certain edges, possibly to signify something.
<figure><img src="image_39.png" /><figcaption>Figure 8.3: From left to right: (i) Focal region by MSI algorithm. (ii) Focal regions by Spectral-Residual algorithm, (iii) Edges detected by TIN algorithm, (iv) Overlay of (iii in green) and edge-detection on original source (iv) (in purple)</figcaption></figure>

8.4.2 The figurative and expressive level

The question what is depicted on a painting can be approached from several angles. One way is to use image recognition and labeling algorithms, such as YOLO [18]. We found however that the current state of the art does not work very well for the kind of paintings made by Luc Tuymans, even though paintings are in the training set. More specialized face-recognition algorithms are often able to recognize that the image depicts a face and recognize the different parts of the face. But they still do not tell us whose face we are looking at.

The second approach is to use reverse image search and similarity search, as provided by search companies such as Google, Bing, Baidu and Yandex. These searches are based on neural network technologies trained with a vast number of annotated digital images uploaded through the internet. Interestingly, these searches are not bringing us closer to the figurative meaning of the painting either. They show many more paintings by the same artist or paintings that are similar (in this case portraits of faces), but none of them comes up with the original source image.

A third approach is to look for textual information, specifically the title of the painting and the description in the catalog. The title is ‘Secrets’ which is indicative of the ultimate motivations of the painter but that turns out not to help us further. However, the description in the catalog [6] starts with the following sentence:

The closed-off inward-looking face of Secrets is in fact that of **Albert Speer ...**

Using this name, a web search quickly brings up a lot of pictures of Albert Speer, including the one that has clearly been used as source. Using the techniques of matching and alignment discussed earlier, this best matching picture can be selected algorithmically from the dozens of pictures that were proposed.

Once the components of a face (mouth, nostrils, eyebrows, etc.) have been recognized it is possible to have more precise descriptions of these components. This recognition is even better if we use the results of color clustering as shown in Figure 8.1 right. There are zones (such as the mustache area) where the painting differs from the original, implying that the painter intended to make this region significant and so these deserve further attention.

Concretely the following areas can be recognized: tight lips, toothbrush mustache, which is a Hitler-like mustache, open nostrils, closed-eyelids, and S-shaped thick eyebrows. Each of these facial characteristics has associated emotional or expressive states (see Figure 8.4). For example tight-lips according to the Cambridge English dictionary is indicative of determined reticence, reluctance to speak, avoiding to show anger, and hiding secrets. (https:
//dictionary.cambridge.org/dictionary/english/tight-lipped). Today such dictionaries are consultable through API's so that they can be accessed by a program which uses the result to further expand the INN.

<figure><img src="image_40.png" /><figcaption>Figure 8.4: Different signifiers from left to right (i) Tight-lips suggest unwillingness to tell something. (ii) Toothbrush mustache suggests masculinity, military, hierarchy. (iii) Open nostrils suggests anger and aggression. (iv) Closed eye-lids suggests denial of reality, hiding secrets. (v) S-shaped thick eyebrows suggests narcissism.</figcaption></figure>

The different elements sketched out here all contribute to additional links in the INN (see Figure 8.5). The different elements are enforcing each other or ambiguities are resolved. For example, the title 'Secrets', the intention of hiding secrets, the closed eyelids and tight lips, all point in the direction of hiding secrets. Closed eye lids can also mean a state of rest or meditation, as in figures of the Buddha, but clearly that is not the intention here.

<figure><img src="image_41.png" /><figcaption>Figure 8.5: Fragment of the INN linking results from image recognition and image processing with characterizations like tight-lipped and interpretations of these signifiers in terms of their expressive or symbolic meaning, for example, that tight-lipped implies hiding secrets.</figcaption></figure>

### 8.4.3 The cultural level and intrinsic motivation

To know what the image of somebody hiding secrets has to do with Albert Speer requires delving deeper into the historical context of the figure of Albert Speer. Here we can call upon other AI resources, namely language processing and semantic web technologies. Off the shelf named-entity recognition algorithms (such as Textrazor https://www.textrazor.com/) are able to identify noun phrases in a text that are descriptions of entities and then provide references
for these entries into the Wikipedia entry https://en.wikipedia.org/wiki/Albert-Speer
or the Wikidata link https://www.wikidata.org/wiki/Q60045 through which then a flood of
information becomes available.

More specifically these resources classify Albert Speer using DBpedia or Freebase descriptors thus providing further entries into semantic web resources that further expand the INN. They add further nodes and links for associated categories and topics and provide weights for them, such as the following all derived from ‘Albert Speer’ through DBpedia:

*   **Categories**: [Conflicts, war and peace>armed conflict>war] (0.80), [arts, culture and entertainment>arts and entertainment>visual arts>architecture] (0.58), [politics] (0.51), [crime, law and justice>crime] (0.50), [conflicts, war and peace] (0.49), [conflicts, war and peace>armed conflict] (0.49), [politics>government] (0.40), [crime, law and justice>crime>war crime] (0.43), [conflicts, war and peace>massacre>genocide] (0.37), [politics>government>defence>military equipment>military weaponry] (0.36).

*   **Topics**: Nazi Germany (1.00), Nazism (1.00), Nazi Party officials (1.00), Leaders of political parties (1.00), Officials of Nazi Germany (1.0), Nazi Party members (1.0), Fascism (1.0), German nationalists (1.0), Nazi architecture (0.91), Antiziganism (0.90), Government of Germany (0.87), Anti-Slavic sentiment (0.80), German nationalism (0.8), Homophobia (0.8), Fascist architecture (0.76), World War II (0.73), Ethnocentrism (0.7), Racial antisemitism (0.65), The Holocaust (0.63), German people convicted of crimes against humanity (0.61), etc.

Figure 8.2 shows a small fragment of the INN for Secrets with the most dominant categories and topics linked into the graph.

By accessing semantic web resources we learn that Albert Speer was condemned for crimes against humanity but always denied reality and tried to hide his own role in the atrocities of the Third Reich. These things connect further with other parts of the exhibition, for example the title ‘La Pelle’ refers back to a book and film by Curzio Malaparte which is also about moral decay. The monumental mosaic work ‘Schwarzheide’ which the visitor sees right before seeing ‘Secrets’ in the Pallazo Grassi exhibition, is based on a drawing made by Kantor while inside the prisoner camp located in Schwarzheide. So the ultimate motivation of Luc Tuymans has to do with the violence of war, denial of responsibility, but also with the double meanings of images. The initial perception of ‘Secrets’ hides a lot of the deeper meanings that get only unlocked when the figurative, expressive and cultural meanings of the painting are elaborated.

8.5 Conclusions

The paper argued that the creation and interpretation of art works involves the construction of an INN, a Integrative narrative network, that contains not only narrative elements at different meaning levels: sensory, motif, expressive, figurative, cultural and intrinsic, but also the intermediary steps necessary to identify possible signifiers and hypothesize their significance, such as the visual processing of the image to extract motifs or to recognize what is depicted. The artist builds up such a network while researching a new painting and then translates parts of this network into physical properties of the art work. He also leaves much implicit. The viewer reconstructs parts of this network, enough to allow it to be integrated into his or her own semantic and episodic memory and thus allow a feeling of understanding.

Much remains to be done to operationalize further the various steps that artists and viewers
take in expanding these integrative narrative networks, but the state of the art in AI is such
that there are now many resources available for doing this and we can expect a lot more to
become possible in the near future. There are some big issues unresolved however, such as what kind of attention mechanism drives the selection of which parts of the INN will be expanded further, how do we avoid not being overwhelmed by information coming from semantic web resources, how to orchestrate the computer vision algorithms so that they provide the most relevant information to proceed with the meaning-seeking process. These are all fascinating open issues for AI research.

The functional approach is providing an alternative path for studying creativity, not based on random choices made in instantiating human-designed or acquired behavioral patterns but on the goal-driven problem solving process that transforms meanings into an artefact. Computational creativity in the domain of art can only be taken seriously when these cognitive considerations, design choices, and design strategies are understood in computational terms and taken into account. I think it is never going to be possible to automate human-like creation or interpretation processes fully because all of human intelligence, memory, and experience is called upon in the creation or interpretation of art, but we can conceive and build many tools to support the creative process or to support viewers, for example, drawing attention to visual features that may go unnoticed, put images in their original context, make information from semantic web resources available *in situ*, etc.

## Acknowledgement

The research reported here was partly financed by the EU FET project MUHAI and the EU FET Starts Initiative for ‘Scientists in Residence of Artist Studios’. The author is grateful to Luc Tuymans for the time spent in joined discussions, the Venice International University, the art-science facilitation agency GLUON and the cultural institute Bozar in Brussels. The close collaboration with Sinem Aslan and Bjorn Wahle on visual processing as well as the many discussions with Oscar Vilarroya on the nature of art, meaning and understanding are gratefully acknowledged.
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# Part V

## Generating Social Narratives
# Chapter 9

## Unexpected gender stereotypes in AI-generated stories§

Laura Spillner*,

*Digital Media Lab, University of Bremen

**Abstract** We investigated gender bias in short stories generated by ChatGPT by generating stories about characters with specified occupations and analyzing the gender assigned to these characters. On the one hand, stereotypes about professions typically associated with women are strongly reinforced, with almost all of the characters in these stories being female, well beyond what would be expected based on human biases. On the other hand, among occupations that humans typically associate with men, the generated stories reinforce these stereotypes in some cases (particularly blue-collar occupations), while reversing them to be strongly stereotypically female in other cases (notably highly regarded professions such as doctors, scientists, attorneys, or astronauts).

**Keywords** Generative AI, Large Language Models, ChatGPT, Story Generation, Gender Bias

### 9.1 Introduction

With this study, we aimed to investigate whether generative AI models such as ChatGPT, when used to generate short stories, introduce bias or amplify stereotypes in their output.

There are two primary ways of bias introduction to consider when analyzing the narratives of generated texts. The first involves biases within the plot of the story, including the choice of gender, skin color, sexuality, and other attributes for different characters, as well as the association of certain actions with these attributes. For example, this may manifest as passive female characters and active male characters. The second form of bias pertains to the language of the text itself, including how characters of different gender, skin color, sexuality, etc., are described, as well as the word choices used in relation to these attributes. We focus here on a straightforward starting point: identifying the occupations that are typically given to male or female characters in stories generated by ChatGPT (GPT-3.5).

The examination of gender bias in generated stories, e.g. for professions, is important for several reasons. Firstly, there already exists a substantial body of research on earlier language models and word embeddings that have explored the same [16]. Additionally, researchers have extensively studied the unequal distribution of women and men in numerous professions, as
well as the stereotypes that lead humans to often associate certain professions with one gender or the other [14, 11].

To analyze gender bias in language models, one standard task is that of reference resolution, where there are two possible nouns (typically occupations or other roles) that a pronoun could refer to, one where the pronoun aligns with the gender stereotype and one where it does not [26]. Another task involves sentence completion, where the model is given the beginning of a sentence (specifying that the subject is e.g. either a man or a woman) and has to complete for example that person's occupation ("The man/woman worked as a ...") [23], after which the sentiment of the resulting sentences is compared between the groups. For machine translation, tests have been done by translating sentences from a gender neutral language that leaves the gender of the subject ambiguous to a gendered language [24]. All of those have revealed the perpetuation or even amplification of societal biases.

However, all of these common tasks differ from that of story generation. To understand if generated stories perpetuate stereotypes, one first has to be able to identify different elements of the plot from the written text generated by the model (such as the gender associated with different characters and their role in the story). Moreover, it is not clear that the biases found with tasks as the ones mentioned above would necessarily be the same as biases in generated stories, since the model might very well draw from different parts of the training data. With the popularity of ChatGPT and the current generation of generative AI, it seems likely that AI-generated stories, short stories or books (including if not especially for children) will be becoming more common in the next years. Understanding whether or not these amplify stereotypes beyond what is common in society thus becomes only more important.

## 9.2 Related Work

Language models tend to mirror human biases, such as gender stereotypes, that are present in their training corpora [6, 21, 17]. This can lead to problematic biases in downstream tasks, e.g. Stanovski et al. [24] showed gender bias in machine translation by examining sentences containing professions, which were translated from a gender-neutral language to a gendered language. While some solutions have been proposed (e.g. amplification can be mitigated through constrains on training corpora [25] and contextualized word embeddings are less biased than static ones [3]), the problem has not been solved. LLMs are just as biased as word embeddings, and can also amplify bias - Bender et al. (2021) argued that they are “stochastic parrots” [4].

There exists a wide variety of benchmarks and tests to measure bias in language models. Zhao et al. [26] introduced the WinoBias benchmark, which focuses on gender bias in co-reference resolution and has become instrumental in evaluating LLMs and highlighting the reinforcement of bias. Another benchmark presented by Nadeem et al. [19] aims to measure both bias and language generation ability at the same time and has revealed the presence of stereotypes in popular text generation models like GPT-2 and GPT-3. Sheng et al. [23] conducted a study using prefix templates such as “the woman worked as...” and employed GPT-2 and other language models to complete these sentences. The resulting sentences were then analyzed for sentiment. The findings showed that, for example, sentences prompted with women were more negative (e.g. using more negatively connotated occupations) compared to those prompted with men, and similarly for aspects such as skin color or sexuality.

While overall the presence of bias in language models has been shown consistently, recent surveys have revealed that many of these metrics are not compatible with each other and produce heterogeneous results, in particular when it comes to embedding-based metrics [9, 12].
Delobelle et al. [9] surveyed many binary gender bias tasks. They point out that while models certainly learn intrinsic bias from training data and show extrinsic bias in downstream tasks, results learned from intrinsic bias metrics cannot easily be generalized to fairness results in downstream tasks, and in particular the templating used in many of these benchmark tasks influences their results considerably [9].

This work primarily focuses on gender bias. Gender bias, both in language models and in linguistic research, often mirrors the stereotypes prevalent in society. For instance, a study by Kotek et al. in 2021 explored gender bias in linguistic example sentences, such as depicting doctors as male and nurses as female [15]. Humans struggle to process sentences when there is an incongruity between the assumed stereotypical gender (based on a specified profession) and the later revealed actual gender of a person [14, 7, 11, 20, 10, 22]. Not only are such stereotypes problematic in and of themselves, they become more concerning when perpetuated by language models that people interact with through chatbots, ChatGPT, and similar systems, which are becoming increasingly common. These stereotypes can influence children's beliefs about the accessibility of certain occupations [5]. Research has been conducted to examine the societal stereotypes associated with different professions or roles according to gender [14, 11]. Kennison et al. [14] created an extensive corpus of roles (occupations as well as other nouns) and their stereotypical gender bias. They asked both male and female participants to rate their perceived gender association with these roles on a scale of 1 (female) to 7 (male).

Recent innovations in language model technology, including models such as ChatGPT and GPT-4, have attracted significant attention in both public and academic spheres due to their enhanced performance capabilities. Liu et al. provide an in-depth overview of research conducted on chatgpt, underlining that these models have improved on numerous natural language tasks [18]. Still, limitations such as the hallucinations observed in ChatGPT [2] still exist. Models such as ChatGPT have used Reinforcement Learning from Human Feedback (RLHF) [8] to address issues of bias and stereotyping in the generated outputs, but apparently this is not sufficient to solve the problem: In a blog post, Kapoor and Narayanan [13] tested GPT-3.5 and GPT-4 on the Winobias benchmark and determined a strong presence of bias in both. Kotek et al. [16] developed a new gender bias benchmark based on the dataset by Kennison et al. [14], and were able to show that ChatGPT not only amplifies existing gender bias compared to the human stereotype ratings, but also provides incorrect answers and explanations due to these biases.

## 9.3 Methods

### 9.3.1 Dataset

We utilized the dataset of Kotek et al. [16], as well as the one by Kennison et al. [14] that the former is based on. Kotek et al. designed a test that is inspired by WinoBias [26], prompting LLMs with sentences such as “In the sentence, ‘the doctor phoned the nurse because she was late’, who was late?” - in contrast to the original WinoBias task, these sentences are ambiguous, but models might use context, syntax or gender stereotypes to answer the question. They constructed a set of 15 sentences with 30 occupation-denoting nouns based on the dataset from [14] and related literature. Kennison et al. [14] collected human gender stereotype ratings for a large corpus of occupations and other roles, which we use as a comparison point of the stereotypical gender humans associate with certain professions. These ratings are on a scale of 1 to 7. The rating scale was explained to participants as such:

“A rating of ‘1’ would indicate that a particular noun is very likely to represent a person who is
female. A rating of ‘7’ would indicate that a particular noun is very likely to represent a person who is male. A rating of ‘4’ would indicate that a particular noun is equally likely to represent a person who is male or female. A rating of ‘2’ or ‘3’ and ‘5’ and ‘6’ would indicate different degrees of likelihood that a particular noun represents a person who is female or male.” ([14], p. 359)

Kennison et al. also used a subset of these (a total of 32 sentences and 64 nouns) in their further reading experiments. Most of the nouns used by Kotek et al. are from this set, with some additional ones included. We combined all of these into one list, and the original dataset by Kennison et al. provides human ratings of the gender stereotypes associated with these professions. We removed duplicate entries when they were very similar as well as nouns that were not professions, but did include professions like “exotic dancer.” This consolidation resulted in 66 professions.

## 9.3.2 Task

We directed ChatGPT to produce stories depicting “a day in the life” of individuals working in specific professions, without specifying the gender of the person. Thus, we aimed to generate stories where there is a straightforward association between gender and occupation. We accessed GPT-3.5 via the API, utilizing the chat completion access point. Our prompt consisted of a system message “You are a writer of short stories”, followed by a user message instructing it to “Write a story about a day in the life of a [profession]”, without any instruction concerning target audience or writing style (see appendix for the exact prompt as well as an example of a generated story).

We conducted 30 rounds of prompts, in each round presenting the professions in a randomized order. This resulted in 30 replies by the model per profession. For some of the professions, we had generated some test stories beforehand - as the prompt remained the same for both the test rounds and the complete set of professions, we included these when analyzing each profession individually. In four instances, ChatGPT refused to fulfill the request, answering (with slight variations) “I’m sorry, I can’t fulfill that request”. Three of those times, the requested profession was “exotic dancer,” while once, it refused to provide a story about a paralegal. In total, our dataset consisted of a total of 2,135 stories, with the smallest number of stories per profession being 27. To calculate the overall statistics we therefore used 27 stories for each profession (randomly selected for those where more stories were generated), so that there was no bias because of the slightly unequal distribution of the professions in the overall dataset.

## 9.3.3 Gender Classification

In theory, identifying the gender of the protagonist of these stories might involve first finding characters in the story, then among those deciding on the main character, and then understanding the gender assigned to them. However, in the case of the ChatGPT-generated stories, no sophisticated story understanding was necessary. Upon examination, we discovered that the stories produced by ChatGPT followed a consistent pattern. Consider the example presented in the appendix. The vast majority of all stories follow the same pattern: They feature a single named character who is introduced right at the beginning, representing the main character associated with the requested profession. In the example, the pronoun “she” appears 15 times, while “he” or “they” appears zero times each (excluding variations like “her”).

Therefore, we counted the frequency of the substrings “ she ”, “ he ”, and “ they ” (including spaces) in the stories and assigned the most frequently occurring pronoun as the character’s gender. There were 31 cases where “they” appeared more frequently than the other pronouns;
we manually reviewed all of them. Among these, 29 were stories were the main character was shown to be working together with another important character, resulting in a higher occurrence of “they”. For these stories we manually identified the gender of the main character. In the remaining two stories, the gender of the main character either is unspecified or was intended to be non-binary (both instances involved the profession of computer programmer). To validate the effectiveness of our method, we conducted a comparison by manually identifying the gender of the main character in a randomly selected subset of 90 stories. The results matched in all 90 stories.

## 9.4 Results

In total, we generated 2,102 stories encompassing 66 different professions. These stories consisted of an average of 393 words ± 55 words. Among the professions examined, the smallest number of stories generated was only 27 for “exotic dancer”. To ensure equal representation of each profession for the overall statistics, we randomly selected 27 stories for analysis for each profession, and calculated the overall gender distribution based on these.

### 9.4.1 Overall Gender Distribution

Our list of 66 professions consisted of an equal number of stereotypically male and stereotypically female professions based on Kennison’s data. Thus, we anticipated that the generated stories would exhibit similar proportions of male and female main characters, both if the stories mirror or even amplify human-held stereotypes as well as if they were to show more equal gender distribution. However, this was not the case: Female characters appeared 1,171 times, while male characters appeared only 609 times. Two stories featured characters with unspecified or nonbinary genders. Therefore, there is a notable overrepresentation of female characters.

### 9.4.2 Gender Ratio per Profession

In the next step, we analyzed the gender ratio for each of the selected 66 professions. We calculated this as the frequency of main characters being male from all stories where the main character was either male or female (thus 0 means all female and 1 means all male). We found that 43 out of the 66 professions had a story gender bias score of less than 0.5 (majority female characters), while 23 professions registered a score greater than 0.5 (majority male characters). The mean rating was 0.34 (STD = 0.39), also showing a bias towards more female main characters.

Subsequently, we visualized the recorded gender bias in stories associated with each profession. Figure 9.1 presents a relative comparison between the stereotype ratings collected by Kennison et al [14] and the bias we identified within the narratives generated by ChatGPT. To make the graphic easily interpretable, we selected a sample of professions rather than a comprehensive list. These professions were selected so as to have a relatively even distribution of human stereotype ratings, from strongly female to strongly male roles: The rating scale developed by Kennison et al. (ranging from 1 to 7) was divided into bins of 0.2 (0.9 to 7.1). We then randomly selected a profession for each bin among those with the smallest distance to the middle of the respective bin. In our sample, none of the professions had human stereotype ratings falling below 1.5 or above 6.3, or between 3.9 and 4.3. Therefore, our resulting data consisted of 22 professions: 12 female-centric and 10 male-centric, as can be seen in Figure 9.1.

The right half of Figure 9.1 illustrates the biases we identified within the narratives that ChatGPT created. As the figure shows, the generated stories amplify the bias held by humans:
<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /><figcaption>Figure 9.1: A selection of professions and their associated gender stereotype rating by humans (left) and the ratio of male to female characters in the respective stories (right). This subset was chosen so that the human ratings are quite evenly distributed from mostly female (1) to mostly male (7). Professions are sorted from most female to most male and colored in red (<4, tends female) or blue (>4, tends male). The right side shows the gender stereotypes in the generated stories as the ratio of male characters out of all characters for this profession, from 0 (all female) to 1 (all male). If the generated stories mirror human stereotypes, these ratios should similarly be quite evenly distributed along the x-axis. Yet, most professions either skew very strongly female or strongly male, with only few closer to the center. Three professions for which human perception veers male here skew strongly female (attorney, doctor, research scientist).</figcaption></figure>
<figure><img src="image_44.png" /><figcaption>Figure 9.2: Scatter plot of human ratings vs gender ratio in the generated stories for all 66 professions. Red area: professions where stereotypical association with women is reinforced or amplified, likewise for the blue area with men. Orange: typically male-perceived professions that in the stories are cast overwhelmingly with female characters (ratio <0.2; human perception >4). Yellow: typically male-perceived professions for which gender ratio in the stories is more equal than human perception (0.4<ratio<0.6; human perception >4).</figcaption></figure>

while this sample of professions is evenly distributed across the scale of human ratings, for
most of these professions the generated stories are either strongly female-biased or strongly
male-biased, with few having a more even gender distribution. The graphic uses red and blue
dots to denote professions considered stereotypically female and male respectively. Interest-
ingly, some stereotypically male roles, such as research scientist and doctor, veered towards a
strong female bias in the AI-generated stories compared to the human stereotypes.

Figure 9.2 presents a scatter plot that directly correlates the human stereotype rating with the bias exhibited in the generated narratives, for all 66 professions. If ChatGPT’s generated stories favored an even gender distribution instead of reinforcing stereotypes, the majority of the data points would align around y=0.5. On the other hand, if ChatGPT reflects the stereotypes perceived by humans, then most data points would reasonably align with the trend line drawn in black (in this instance, y=(x-1)/6, as x ranges from 1 to 7 and y varies from 0 to 1). However, neither of those are the case.

For professions typically associated with women, this stereotype is strongly amplified in Chat-
GPT's stories. Every red data point sits below the trend line, showing that ChatGPT associates
these jobs with women more strongly than most humans do. Remarkably, all but five points are
at y=0, which are professions where the stories exclusively feature female characters. Two pro-
fessions (cashier and teacher) are marginally above y=0, and three others (bookkeeper, clerk,
high school teacher) reside below the trend line, albeit nearer to it.

Concerning professions often associated with males, we can identify three predominant clusters:
The first subset reveals amplified male stereotypes (above the trend line), including professions such as bartender, basketball player, bellhop, butcher, carpenter, coach, computer programmer, and more. The second subset includes jobs commonly perceived as male-dominated, yet the bias in ChatGPT's stories is predominantly female (20% of the stories feature male characters) - essentially, ChatGPT reverses the stereotype. This group includes the professions astronaut, attorney, dentist, doctor, lawyer, research scientist, and tattooist. The third subset includes jobs typically perceived as male' by humans, but which nearly attain gender parity in the generated stories. These professions, including banker, chef, executive, high school principal, history professor, movie director, pilot, and professor, are between 40%-60%, except for high school principal, which is slightly higher at 60.6%.

## 9.5 Discussion

We chose the task of story generation to investigate gender bias in regards to professions in ChatGPT (GPT-3.5). While many typical binary gender bias metrics and benchmarks utilize template sentences or reference resolution tasks, these methods have been criticized in part because of inconsistency in results due to this templating [9]. Analyzing gender stereotypes in generated stories might be one way to counter these problems. This approach uses a test case comparable to common downstream tasks, thus testing explicitly the bias that the LLM might introduce in practice, and allows for more diverse domains to be studied based on the story prompt.

The results of our investigation highlight two significant findings. Firstly, it is evident that ChatGPT-generated short stories greatly intensify gender stereotypes associated with occupations, even more so than human perceptions of gender roles in the same professions. This reflects the findings by Kotek et al. [16]. Despite efforts to remove bias from the models during training and using data that include conventional bias tasks such as the Winobias dataset, the bias amplification is not decreasing compared to earlier models.

Secondly, there is a stark difference between male and female stereotypes observed in this study. While gender bias is maintained or even strengthened in the case of typically female jobs, the bias towards stereotypically male occupations is sometimes inverted, casting them as typically female roles in the generated stories. Analyzing the occupations where male gender bias remained and those where it was either reversed or is almost equal, a pattern emerges. Many jobs retaining their male bias are blue-collar or might be perceived as "lower-status". Conversely, those reversing or neutralizing bias are predominantly roles seen as high-status. Furthermore, we noted that many of the roles which saw bias reversed tended to be more stereotypical or emblematic jobs that are frequently portrayed in the media and literature, such as doctors, pilots, astronauts, professors, and lawyers (consider e.g. that doctor vs. nurse is a typical example for gender bias in language models).

This second effect we observe may be an outcome of the reinforcement learning from human feedback applied by OpenAI. It stands to reason that workers could have rectified gender biases associated with professions such as doctors or lawyers, as they might be more aware of existing biases and of efforts to encourage women to take up these professions. However, this correction was not universally applied to all stereotypically male occupations. In the cases where it was applied, this adjustment may have inadvertently lead to an inversion of roles, evidenced by the fact a staggering 97% of narratives featuring doctors portray women characters.

There are more narratives predominantly featuring female characters and assigning high-status roles to female leads, whilst stereotypical male or blue-collar employment remained male. However, more commonly female-dominated roles are chiefly represented by women, strongly am-
plifying stereotypes about occupations already linked with women such as hairdressers, mani-
curists, or florists, jobs that are traditionally deemed “women’s work” such as nannies, nurses,
or housekeepers, and even occupations with minor societal bias, for instance, bank tellers or
paralegals. While the model does extend some of the professions stereotypically associated
with men to female characters, the same is not the case the other way around - which mirrors a
form of gender bias common in society as well, in which particularly occupations deemed very
feminine are perceived as demeaning or emasculating for men. Worrying is the extent to which
the effect in the generated stories goes well beyond the stereotypes that humans associate with
the same professions.

It is unclear to us where this effect stems from. We hypothesize that it might be an effect of
the RLHF training used for ChatGPT, introducing what is essentially an overcorrection based
on previous criticism on gender bias in language models. At the same time, however, it might
very well be based on statistics based on subsets of the training data as well. While these
differences do not mirror human biases, do they maybe mirror trends in short stories published
online that were used for the training? With closed-source models such as ChatGPT, this is
difficult to establish.

9.6 Conclusion

The main finding of our experiment is the stark difference in how stereotypes are perpetuated when it comes to female vs. male characters. The stories unexpectedly feature a majority of female characters (66%), and for high-status occupations common in stories, such as doctors, often features overwhelmingly female characters (for this profession 97%). At the same time, many less highly regarded occupations that are also considered stereotypically male by humans are strongly reserved for male characters, also amplifying these stereotypes well beyond human biases. Moreover, almost all of the occupations considered more stereotypically female are practically exclusively given female characters, amplifying existing biases for professions such as nurses or secretaries well beyond the stereotypes associated with them by humans.

Interestingly, this difference has to our knowledge not been seen in other studies focused on
tasks such as reference resolution. As far as we know, this effect is unique to generated stories,
and certainly concerning. In this study we only considered one simple example - there are
many other ways in which stories can perpetuate stereotypes that would be more difficult to
analyze, such as through character traits or roles of different characters in the plot and many
other attributes assigned to different characters.

In the future, it would certainly be interesting to experiment more broadly with bias detection
in stories generated from LLMs. We only tested ChatGPT, for which it has been established
that guardrails and RLHF have been used to try to mitigate bias, which might have been
one reason for the over-correction we found in regards to female story characters. Investigating
other LLMs and in particular open-access language models might shed more light on the sources
of this bias. One other direction we did not test deeply is the effect of the prompt itself. The
description we used was intended to be relatively neutral, and it stands to reason that variations
in the prompt could influence the outcome in terms of gender bias. We did conduct some tests
with a variation where we asked for “children’s stories”, but the observed bias was quite similar
to the standard variation, and we did not analyze this variant further at this point.
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# Chapter 10

## Narrativizing Knowledge Graphs

Robert Porzel*, Mihai Pomarlan*†, Laura Spillner*, John Bateman† Thomas Mildner* Carlo Santagiustina‡△

*Digital Media Lab, University of Bremen

† FB10, University of Bremen

‡ Venice International University, Italy

△ Ca'Foscari University of Venice, Italy

### Abstract

Any natural language expression of a set of facts – that can be represented as a knowledge graph – will more or less overtly assume a specific perspective on these facts. In this paper we see the conversion of a given knowledge graph into natural language as the construction of a narrative about the assertions made by the knowledge graph. We, therefore, propose a specific pipeline that can be applied to produce linguistic narratives from knowledge graphs using an ontological layer and corresponding rules that turn a knowledge graph into a semantic specification for natural language generation. Critically, narratives are seen as necessarily committing to specific perspectives taken on the facts presented. We show how this most commonly neglected facet of producing summaries of facts can be brought under control.

**Keywords** Narratives, Ontologies, Cognitive Systems, Framing

“Every three years the holy man from the mountain came to the village.”

### 10.1 Introduction

In the popular novel *Stranger in a Strange Land* Robert Heinlein introduces a cast of people who have been trained to speak only non-subjective truths containing neither valence nor assumptions. When describing a war-like situation it might, theoretically, be possible to say that, for example, some governmental head of a country gave an order to the army to move into another country by force. Natural language renditions of corresponding states of affairs usually contain expressions such as *invading* or *liberating* that assume a specific perspective (taking sides) and denote some valuation of the situation at hand. In other words, rather than objective and neutral truth-sayers, we are spinning narratives out of the facts on the ground.

The concept of a *narrative* has migrated from its original domain in the literary sciences to a multitude of diverse and increasingly distant research fields. It has become an important
element in research on games [11] or in history [14] to name a few of these domains. At long
last it has also arrived in the cognitive sciences where narratives are regarded to be a central
means of sense making [15]. From there it was merely a short jump over to the field of computer
science where the concept is employed to describe semantically annotated episodes of recorded
activities [5] and has, subsequently, been formalized using description logics [24].

When we make assertions about people or events to capture them in a knowledge graph, we
accumulate information that is supposed to represent the ground truth. In narratology this is
often called the *fabula* [8]. This *fabula* can represent episodes, e.g.:

* for logging an autonomous robot the *fabula* can represent trajectories of body parts and activity-specific force events [18].

* for modeling historical or current events, as done by the EventKG knowledge base [13], the *fabula* usually consists of events and their participants.

While these fabulae contain large quantities of information they are, by themselves, not very
meaningful. Only when we put them into a pragmatic context do we assign additional meaning
to them. For example, we can interpret the same observed episode as either *throwing something*
or *dropping something*. This difference in the narrativization, consequently, yields two distinct
narratives:

(1) Sherlock dropped the glass onto the floor

(2) Sherlock threw the glass onto the floor

It is important to note that the knowledge graph representing these two minimal narratives can
be identical. We consequently differentiate between a factual knowledge graph, i.e. the *fabula*,
which has not been narrativized and a (language-based) description of it, i.e. the *narrative*.
This pairs a situation with a selected conceptualization, i.e. interpretation, thereof and renders
the latter in natural language. In addition to becoming meaningful, the description will, in
turn, evoke a pragmatic stance that ascribes, for example, a specific perspective and intention
to the agent(s) acting in specific roles within the narrative. The more general contribution of
this work is to examine certain elements of narrative mechanics, as part of a larger effort to
understand the mechanics of conflictual narratives [26]. Specifically, we provide a technological
scaffolding for the process of constructing such narratives in order to further empirical research
on how narratives emerge in the wild. In the following, we will, therefore, present a system
that takes a knowledge graph, i.e. a *fabula*, as input and converts it to a narrative.

10.2 Related Work

Several approaches exist for expressing formal representations of narratives. Each of these
approaches is driven by the specific requirements of the given application at hand. For example,
the model of Meghini et al. seeks to organize information provided in digital libraries and,
therefore, models both fabulae as well as narratives as events and allows for events to feature
dependent events [8]. The model uses RDF based on OWL and narrative events can have spatial
or temporal relations between them, but the main purpose is to connect digitally represented
entities to pertinent events, e.g. the Divine Comedy as a book and the person Dante Alighieri
can be connected by a narrative *Dante writes the Divine Comedy*. In this approach *events* are
also not formally specified as no foundational framework is employed.

A different approach is the work described by Evans et al. that seeks to classify (partial) sensory
data as a narrative that can be framed as an inductive logic programming task [10]. While
their focus lies on reducing the search space by finding hypotheses – which equal narratives in
their approach – that provide as simple an explanation of the observed data as possible. This approach can, therefore, be deemed compatible yet orthogonal to the one presented herein, as it focuses not on representing and using narratives via an ontology, but on a classification approach that employs such a model as a target representation.

Closer to the current task is the work described by Kroll et al. that makes a useful distinction between factual relations, as expressed by knowledge graphs, and narrative relations that constitute hypothetical relations connecting factual ones [20]. For this, the approach needs to employ RDF* to express relations that range over relations. This approach can, therefore, be employed to postulate, for example, causation relations as a narrative that connects hitherto isolated knowledge graphs.

For our task at hand, we employ a model of narratives that provides a suitable ontological theory and subsequent model and is based on the Socio-physical Model of Activities (SOMA) [24]. SOMA already comes with the central distinction between ground and descriptive entities [6]. This useful distinction is provided by the foundational layer, which in this case is the Dolce Ultra Light framework with the addition of the Descriptions and Situations Module (DUL+D&S) [12].

For generating comprehensible and appropriate natural language expressions various end-to-end approaches based on some forms of machine learning exist, both for generating individual sentences based on knowledge graphs [22] as well as for sequences of sentences for sub-graphs retrieved from larger knowledge graphs [21]. The work presented in this paper is, however, closely related to that of constructing formal narrative structures out of distributed knowledge graphs [7]. In the symbolic approach, introduced in this work, we employ the Komet-Penman MultiLingual (KPML) system to add a natural language generation component. This system offers a well-tested platform for grammar engineering that is specifically designed for natural language generation [25]. KPML employs large-scale grammars written within the framework of Systemic-Functional Linguistics (SFL). The employment of SFL allows us to include linguistic phenomena which are important for the generation of natural texts and which go beyond the bare propositional content that is to be expressed [2].

## 10.3 From Knowledge Graphs via Narratives to Natural Language

The method we propose and showcase in this work features two main steps that follow each other:

* Firstly, a formal representation of a narrative as triples is constructed based on an existing knowledge graph of a given event (the *fabula*). For this, the events and their participants as defined in the fabula are linked to different narratives that describe these events and, consequently, can consider them from different perspectives. By filtering based on a given perspective and choosing which events and participants to include, the narrative itself is constructed. In narratology the result of this filtering is usually referred to as the *plot* [8].

* Secondly, the narrative - in the form of said triples - is converted into a semantic specification of the text to be produced, which is then converted into text using a natural language generation system based on KPML and systemic functional grammar[2].

In the following, we provide further details of our approach for turning knowledge graphs into natural language text using examples from the domain of everyday activities and current world affairs.
### 10.3.1 Steps

Figure 10.1 shows an overview of the steps necessary to go from the underlying knowledge graph to the generated natural language text, which describes a chosen event from a specific narrative perspective.<fnref n="1" />

**Fabula** The underlying knowledge graph, which does not explicitly encode any kind of perspective on the given events, is what we consider as the *fabula*. In the future, we aim to use existing resources, such as EventKG [13], as the basis for constructing formal narratives of events. However, since existing resources include large amounts of knowledge but also often miss important relations, this would still require a significant amount of manual work. We, therefore, present this case study on small-scale examples, for which we constructed the base knowledge graphs manually using the information available in EventKG.

**Plot and Narrative** The selected knowledge graph is expanded into narratives that can assume different perspectives with respective EVENTS that are mapped to TASKS and PARTICIPANTS that are mapped to their respective ROLES. This content selection and filtering based on perspective produces a *narrative specification* represented as triples. Figure 10.3 gives an overview as to what sort of entities and relationships can appear in narrative specification triples.

**Semantic Specification** The narrative specification triples are mapped to semantic structures such as discourse relations between events (motivation, explanation, concession), action specifications, object descriptions; assembling these structures results in a semantic specification, which is a feature structure using concepts from the Generalized Upper Model (GUM) and Upper Interaction Ontology to represent its elements [3].

**Tactical Generation** Once a semantic specification exists, it is transferred to the language generation software KPML to produce finely-controlled natural language text.

<figure><img src="image_45.png" /><figcaption>Figure 10.1: The steps of converting a fragment of a Fabula into narrativized natural language text.</figcaption></figure>

### 10.3.2 Narrativization of Knowledge Graphs

Figure 10.2 shows a very small set of events and the participants included in them. In prior work [26], a large corpus of tweets concerning the ongoing war in Ukraine was collected. This data was analyzed in order to reconstruct different narratives surrounding this conflict which have been shared on social media. Since this existing dataset includes texts written by humans

<footnote n="1">¹In this paper concepts that are terms of the SOMA ontology are denoted by setting their labels in SMALL CAPS.</footnote>
describing these events from many different perspectives, we used these events as a starting point that is formalized for this example. A listing of such knowledge graphs representing specific events is provided in the following table; these are then construable into the narratives depicted in Figure 10.2

<table><thead><tr><th colspan="2">Event Knowledge Graphs (Input)</th></tr></thead><tbody><tr><td>(E1, hasParticipant, Ukraine(people))</td><td></td></tr><tr><td>(E1, hasParticipant, Ukraine(government))</td><td></td></tr><tr><td>(E1, hasParticipant, Russia)</td><td></td></tr><tr><td>(E2, hasParticipant, Ukraine(government))</td><td></td></tr><tr><td>(E2, hasParticipant, NATO)</td><td></td></tr><tr><td>(E2, hasParticipant, Russia)</td><td></td></tr><tr><td>(E3, hasParticipant, NATO)</td><td></td></tr><tr><td>(E3, hasParticipant, Ukraine(government))</td><td></td></tr><tr><td>(E3, hasParticipant, Russia)</td><td></td></tr><tr><td>(E4, hasParticipant, Ukraine(people))</td><td></td></tr><tr><td>(E4, hasParticipant, Ukraine(government))</td><td></td></tr><tr><td>(E4, hasParticipant, Russia)</td><td></td></tr></tbody></table>

As proposed in the formal model adopted for representing narrative [24], narratives define TASKS which are executed in the given EVENTS, and the ROLES that the events' participants can take. Based on this formal theory of narratives, we consider several ways in which a neutral knowledge graph can be narrativized:

<figure><img src="image_46.png" /><figcaption>Figure 10.2: Ways to narrativize a given event knowledge graph. Clockwise from top-left: adding event type; adding roles to participants; adding relationships between events; selecting terminology/part-whole substitutions.</figcaption></figure>
**Event - Task** An event that exists in the KG can be construed in different tasks: for example, E1 in Figure 10.2 represents the invasion of Ukraine by Russia, which, depending on the speaker's point of view, might be characterized e.g. as an invasion, the launch of a special military operation, or even as a liberation.

**Participant - Role** A narrative also defines at least one role in the task which is taken by the participants of the event. Thus, different narratives might include very different kinds of roles, e.g. an invader *versus* a liberator, as well as having different participants take those roles, e.g. in E2, the escalation of the Russia-Ukraine crisis, the role of the agent causing the escalation might be taken by Russia, by NATO, or by other participants, depending on the point of view being constructed by the narrative.

**Terminology** Terms used to refer to events or their participants can impact the surround-
ing narratives in different ways. Firstly, collections of knowledge graphs, such as provided by
EventKG, which combine information from several distinct sources, provide different terms for
the events themselves by combining events from different sources through the “sameAs” rela-
tionship. For example, depending on the source, the ongoing war in Ukraine is referred to as
“Ukrainian War”, “Russo-Ukrainian War” or “Russian military intervention in Ukraine”; and
the annexation of Crimea can be found under “Annexation of Crimea by Russian Federation”,
“Crimean Crisis 2014”, or “armed political conflict surrounding Crimea”. Additionally, alterna-
tive names are also provided, including “Secession and Incorporation of Crimea” as referring to
the annexation of Crimea, or “Russian Spring” which refers to the pro-Russian unrest in Ukraine
in 2013. Secondly, the events included in EventKG also have a “type” - thus, the Ukrainian
war might be seen not only as a war, but also as an armed conflict, a military conflict, or an
“intervention”; while the aforementioned unrest in 2014 is defined e.g. as “secession”, “protest”,
“civil disorder”, or “insurgency”. Thirdly, terminology can be used to characterize the partic-
ipants in the event. This ranges from smaller distinctions such as the usage of a person’s full
name or title (e.g. “Putin” vs. “President Vladimir Putin”, vs. “dictator Putin”), to terms
that also cast participants in very specific roles regarding the task defined in the narrative: an
example of this in the Twitter conversation surrounding the war in Ukraine is the casting of
its invasion as a liberation - however, not only is the invasion itself characterized as aiming to
liberate the country in general, but the oppressor it is to be liberated from is “the Nazis”. This
term can be found in different contexts. In tweets talking about “liberation from the Nazis”,
the latter usually refers more generally to the Ukrainian government, which thus takes the role
of oppressor in the liberation task. However, when this narrative is challenged, the term is
defended by refocusing it on other groups, usually the Azov regiment of the Ukrainian Army
[26].

**Relationships between events** In EventKG, the events themselves are connected only through sub-event relationships, but not in terms of their causality or other relations. The combination of several events in textual expressions can add additional connections that are by and large contingent on the narrative perspective: an event might be caused by a different one, be motivated by another or happen in spite of another event that opposes it. For example, depending on the perspective of the speaker, the invasion of Ukraine by Russia (E1) might be caused by continuously escalating tensions (E2) to which Russia is reacting, or it might be started by Russia with the goal of further escalating the existing crisis.

**Perspective Filtering** Consequently, through the steps described above, a perspective has been chosen, that primarily involves the selection of a frame-giving conceptualization of the event. In the terminology of SOMA this corresponds to selecting a TASK that *classifies* an
EVENT. Analogous to frame entities found in FrameNet [1], each specific frame contains a frame-specific configuration of *roles*, e.g. invading features invaders and invaded entities whereas liberating involves the liberated and the liberators. Correspondingly, one and the same entity can be endowed with a positive valence, e.g. the liberator, or a negative one, e.g. the invader, albeit both are instantiated by the same entity in the knowledge graph. A set of sample configurations is given in Figure 10.2.

### 10.3.3 From Narrative Specification to Texts

As previously mentioned, for our purposes here a narrative specification is a collection of triples asserting that some events happened and had various entities as participants. The events may have been connected to each other by relations such as:

* opposition - one event should have prevented another but did not

* motivation - one event happened because its agentive participants desired some other event to happen

* explanation - one event happened because another one happened

A summary of the available relationships and entity types is given in Figure 10.3.

<figure><img src="image_47.png" /><figcaption>Figure 10.3: The ontological schema of a narrative specification. The main entities are events, which can be of different types, and which have objects as participants. Events and objects can have qualities.</figcaption></figure>

For the final part of the language generation process, i.e. the realization, we use KPML [2]. KPML does not accept triples as inputs, but rather semantic specifications (semspecs), which are represented as feature structures. An example such semantic specification for “the cup” is as follows:

(OBJ_0b4ax4 / |Object| :LEX CUP :determiner the)

A semspec contains an identifier for the entity it describes, an ontological characterization in terms of a semantic type defined in the Generalized Upper Model or in the Upper Interaction
Ontology, and various other lexico-semantic information such as a lexical item to use in the realization, determiners for objects, flags about identifiability and so on. Semspecs can include other semspecs, or refer to them via identifiers. In particular, a semspec for an event or object will typically include semspecs for entities playing some role for that event or object.

The conversion from a set of triples – the narrative specification – to a semspec makes use of the compositionality of KPML semspecs. That is, to generate a semantics specification for a relationship between events it is enough to generate a semspec for each event participating in the relation, and then generate a semspec for the relation according to an appropriate template which is then filled in with the semspecs for the events. Likewise, to generate a semspec for an event is to fill in an appropriate template with the semspecs produced for the participant objects and so on.

In the table following we list some example narrative specs produced for the knowledge graphs augmented as shown in Figure 10.2 above and the corresponding KPML outputs, i.e. natural language expressions, produced from them. The semspecs themselves tend to be more verbose and we omit them for reasons of space. The interested reader can find these examples, and more, at our github repository for KPML examples<fnref n="2" />. One note about the examples: each entity also has associated a lexical item via a “hasLex” property. For space reasons, these were not listed as for these examples the entity names are enough for the reader to infer the lexical item.

<footnote n="2">²https://github.com/mpomarlan/KPML_examples. A copy of KPML, with recent patches, is available upon request.</footnote>
<table><thead><tr><th>Narrative specification</th><th>Natural language output</th></tr></thead><tbody><tr><td>('construedAs', 'invade', 'dispmatact') ('hasAgent', 'invade', 'russia') ('hasPatient', 'invade', 'ukraine') ('hasTense', 'invade', 'past')</td><td>Russia invaded Ukraine.</td></tr><tr><td>('construedAs', 'liberate', 'dispmatact') ('hasAgent', 'liberate', 'russia') ('hasPatient', 'liberate', 'ukraine') ('hasOpponent', 'liberate', 'nazis') ('hasTense', 'liberate', 'present-continuous') ('hasDeterminer', 'nazis', 'the')</td><td>Russia is liberating Ukraine in spite of the Nazis.</td></tr><tr><td>('construedAs', 'liberate', 'dispmatact') ('hasAgent', 'liberate', 'russia') ('hasPatient', 'liberate', 'ukraine') ('hasSource', 'liberate', 'nazis') ('hasTense', 'liberate', 'present-continuous') ('hasDeterminer', 'nazis', 'the')</td><td>Russia is liberating Ukraine from the Nazis.</td></tr><tr><td>('construedAs', 'launch', 'dispmatact') ('hasAgent', 'launch', 'russia') ('hasPatient', 'launch', 'operation') ('hasDestination', 'launch', 'ukraine') ('hasSize', 'operation', 'special') ('hasMatQuality', 'operation', 'military') ('hasTense', 'launch', 'past') ('hasDeterminer', 'operation', 'a')</td><td>Russia launched a special military operation to Ukraine.</td></tr><tr><td>[narr.spec for ``Russia invaded Ukraine''] ('construedAs', 'escalate', 'dispmatact') ('hasAgent', 'escalate', 'russia') ('hasPatient', 'escalate', 'crisis') ('hasQuality', 'crisis', 'ongoing') ('hasTense', 'escalate', 'present') ('hasModality', 'escalate', 'can') ('isMotivatedBy', 'invade', 'escalate')</td><td>Russia invaded Ukraine, so that Russia can escalate the ongoing crisis.</td></tr><tr><td>[narr.spec for ``Russia liberated Ukraine''] ('construedAs', 'escalate', 'dispmatact') ('hasAgent', 'escalate', 'nato') ('hasPatient', 'escalate', 'crisis') ('hasQuality', 'crisis', 'ongoing') ('hasTense', 'escalate', 'past') ('isExplainedBy', 'liberate', 'escalate')</td><td>Russia liberated Ukraine, because NATO escalated the ongoing crisis.</td></tr><tr><td>('construedAs', 'leave', 'nonaffspat') ('hasAgent', 'leave', 'people') ('hasSource', 'leave', 'ukraine') ('hasDestination', 'leave', 'russia') ('hasTense', 'leave', 'present-continuous') ('hasDeterminer', 'people', 'the')</td><td>The people are leaving from Ukraine to Russia.</td></tr><tr><td>('construedAs', 'abduct', 'dispmatact') ('hasAgent', 'abduct', 'russia') ('hasSource', 'abduct', 'ukraine') ('hasPatient', 'abduct', 'people') ('hasTense', 'abduct', 'present-continuous')</td><td>Russia is abducting people from Ukraine.</td></tr></tbody></table>

## 10.4 Conclusion and Future Work

In this preliminary and ongoing work, we have introduced a pipeline for turning knowledge graphs into natural language texts that assume a specific perspective on the propositional content given by the respective knowledge graph. For this, a formal model of narratives provides the ontological foundation for representing the intermediate perspective-specific representation. It should also be noted that not only do natural language expressions contain biases and value judgments, but also ontological models can feature biases imported by their designers [17]. In the present work, however, the perspective is explicitly modeled as an integral part of the narratives at hand. This feature enables us to produce narrative-specific descriptions of a given knowledge graph that, as opposed to, for example, tweets in the wild, makes the underlying perspective openly available for inspection via the narrative specification.
Our approach, however, still needs more work to make it scalable and generally applicable to
any knowledge graph available in some collection. For this ways have to be found for:

* dealing with missing information in knowledge graphs, for example, in EventKG the person Louis the XVIth is not given as a participant of his own beheading.

* automatic creation of hermeneutic filters for mapping the events contained in a knowledge graph to possible interpretations thereof. One approach for this could employ generic ontology design patterns [19].

* adding register to the natural language generation output [4]. Lexical choices can also express sentiments and ideological positioning about some entity, e.g. the difference between referring to an atom bomb as a tactical *versus* a nuclear weapon carries some opinion with it [16].

To evaluate the output of our system a type of *Bleu Score* evaluation could easily be undertaken [23]. However, since it is not the intent of this work to create textual narratives for human readers, an evaluation of their structural similarity to real conflictual narratives [26] would be more appropriate. For this some annotation-based metrics, e.g. Cohen's Kappa [9], could be employed to compare semantic annotations of real and generated narratives. We hope this work constitutes the beginning of a research effort that complements current efforts that focus on going from natural language expressions to formal specifications - either narratives or knowledge graphs - by looking at the reverse direction. Ultimately, the long-term research effort behind this undertaking concerns an improvement of our understanding of narrative mechanics, i.e. how they are constructed, manipulated and finally expressed as natural language utterances.
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# Part VI

## Looking Outward and forward
# Chapter 11

## AI in the public debate: the perspective of AI researchers

Folco Soffietti*, Alessandra Fornetti*, Ilda Mannino*

*Venice International University

### Abstract

Discussion of Artificial Intelligence (AI) has increased sharply since 2009 with both optimistic and pessimistic views. In recent years many specific concerns related to AI advancements (e.g. ethics, negative impact on job availability, misinformation, inequalities) have been growing [5].

But what is the role of AI researchers in promoting a reliable, trustworthy and effective communication of AI? What are the elements in AI communication strategies that can support a constructive engagement with AI? How can quality communication of AI support the current debate on science communication, especially in relation to scientists' engagement?

This chapter will try to offer answers to these questions, building on the MUHAI<fnref n="1" /> project experience. The answers are obtained from a direct exchange with AI scientists involved in the project, data related to the project communication activities, and existing literature on the topic. Interviews with key actors, and specifically MUHAI researchers provide relevant insights to define how to promote quality communication of AI. The objective of this chapter is to contribute to the understanding of how the aspects of AI communication can enter the ongoing discussions on the role of researchers and research institutions in the current public debate.

**Keywords** Artificial Intelligence, Science Communication, Ethics

### 11.1 Introduction

Between 2020 and 2024, the MUHAI project played a pivotal role in exploring new frontiers in AI research, coinciding with the rise of generative AI models accessible to the public. This surge in AI presence reshaped public discourse, extending beyond expert circles to dominate media and social platforms. As AI became a normalized topic in society [8, 9], the media's role in bridging the gap between AI advancements and public understanding became increasingly critical [15, 18].

<footnote n="1">¹https://www.univiu.org/focus-areas/science-communication-and-education/muhai-project</footnote>
The growing public demand for AI-related information laid fertile ground for research on how scholars positioned themselves in these discussions. Researchers, especially in academia, have been central to the AI debate, with the UK as a notable example [3]. Ethical concerns, including the societal role of AI and its impact, became a focal point of this discourse [6, 13, 2, 10].

In the note for the Royal Society event² of 2020 "Communicating AI: the role of researchers in enabling public conversations about AI" had pointed out that the utopian or dystopian visions of the future that define many of today's AI narratives tend to conjure specific images of AI, and that a key challenge for researchers seeking to contribute to public discussions is therefore the need to convey nuance. On the same note it is affirmed that the presence of a range of voices that are able to provide accurate background information about – or de-bunking myths around – advances in AI can help improve the evidence base for public debate.

The work performed in MUHAI was aware of current Ethics debates and the need to disseminate information towards policy makers in a moment of general turmoil in aligning national and international legislations to the rise of AI [11]. In addition, the consortium understood that the project would contribute to a broader discussion on the creation of AI imagery [14].

MUHAI sought to reshape the narrative by focusing on a 'human-centric AI' approach, distanc-
ing itself from generative AI and fostering a more trustworthy relationship between humans
and machines [17]. The project addressed key ethical and societal questions, particularly the
influence of AI on social media discourse and public opinion.

MUHAI's communication efforts were comprehensive, integrating communication, dissemi-
nation, and engagement with the ethical, legal, socio-economic, and cultural dimensions of
AI (ELSEC). Its practical activities spanned public engagement, educational outreach, and
business-community collaboration. Noteworthy efforts included digital outreach through blogs,
videos, and campaigns such as "Word of the Month" and "TarotAI," while the project's web-
site employed a warm, approachable visual identity. Although online engagement figures were
modest, they aligned with trends in other EU-funded AI projects from the same period.

<figure><img src="image_48.png" /><figcaption>Figure 11.1: The visual identity of MUHAI, designed with the aim to offer a representation of AI as more human-centered and “warm”. Studio +Fortuna, 2021</figcaption></figure>

With over 120 events and interactions with media, policy makers, and the arts by May 2024, MUHAI's communication strategy fostered important discussions on AI, positioning the project as a case study in building trust and enhancing the relationship between AI researchers and communicators.

²https://www.royalsoc.ac.uk/-/media/policy/projects/ai-and-society/
how-we-talk-about-ai-and-why-it-matters-workshop-notes.pdf
<figure><img src="image_49.png" /><figcaption>Figure 11.2: Sample from the "People" campaign, presenting the humans behind the research on AI.</figcaption></figure>

## 11.2 Beyond project communication: investigating the social debate

The project was seen, from the perspective of science communicators responsible for the communication activities, as an opportunity to investigate how researchers in AI positioned themselves in the communication scene. An important point to understand their participation to the public debate and how science communication theories might support them. The following aspects were taken into consideration:

* AI researchers' understanding of the role of public communication to support the impact of their research and influence how societies respond to technological advances;

* if and how the experience in communicating research changes AI researchers' awareness of the role of communication for end-users and society;

* how scicommers can support AI researchers in communicating AI.

These research focuses were defined from a set of broader research questions regarding the AI word. *Especially, what is the role of AI researchers in promoting a reliable, trustworthy and effective communication of AI? What are the elements in AI communication strategies that can support a constructive engagement with AI? How can quality communication of AI support the current debate on science communication, especially in relation to scientists' engagement?*

To answer these questions 12 researchers, 10 seniors (2 of which from industry-based research) and 2 junior - 5 women and 7 men - were asked five questions to assess their viewpoint about the role of researchers in communication activities regarding AI.

The five questions ranged from general educated opinions (and experiences) about communication of AI in society to project-specific questions to assess the project's strengths and weaknesses in communication.

1. Can you share your view regarding the communication of AI in society?
<figure><img src="image_50.png" /><figcaption>Figure 11.3: Figure 2: examples of MUHAI campaigns, “People”, videos putting researchers (humans) under the lightspot, “Word of the Month” making AI language more understandable, and “FuturAI” presenting researchers’ perspective on future development with the cultural twist of Mantegna’s tarots. VIU and Studio +Fortuna</figcaption></figure>

2. In your view what is the role of AI researchers in promoting a trustworthy and effective communication of AI?

3. What are the elements in AI communication strategies that can support a constructive engagement with AI?

4. Did the MUHAI experience change your awareness on the role of researchers' communication of AI for end-users and society?

5. In the MUHAI experience what kind of support from communicators you consider most valuable and effective for scientists in AI? What could have been improved?

The five questions were identified as sufficiently generic to support qualitative research. The questions were submitted to all researchers in the same oral format.
## 11.3 Results

What insights can be drawn from this research? To enhance accessibility to the key findings, the main messages have been organised into five thematic clusters aligned with the research questions: Communication of AI in Society, The Role of AI Researchers in Building Trustworthiness, Communication Strategies to Foster Constructive Engagement with AI, MUHAI as a Catalyst for Raising Awareness About Researchers' Roles as Communicators, and The MUHAI Experience and Support from Communication Professionals.

### 11.3.1 Communication of AI in Society

The communication of AI in society, as seen through the perspectives of AI researchers, reveals a dual landscape marked by both excitement and apprehension, often shaped by historical contexts and current trends. Historically, AI discussions were primarily confined to specialized expert circles, where a more balanced understanding of its potential benefits and drawbacks prevailed. However, the advent of technologies like ChatGPT has thrust AI into the mainstream, creating two distinct narratives: one aimed at experts, which remains more grounded, and another directed at the general public, which is often more optimistic and exaggerated.

The rise of AI-driven articles and discussions by tech enthusiasts rather than scientists has fueled an optimistic narrative in businesses, amplifying the belief that AI will revolutionize society. On the other hand, certain communities, especially those in the arts and creative fields, have expressed significant mistrust, particularly among younger generations concerned about AI's impact on their work. Public discourse often centers on societal issues like privacy, job displacement, and AI's potential for control, while scientific voices struggle to offer nuanced perspectives.

AI research-communication is heavily corporate-driven in the Anglo-American world, where universities emulate these strategies to boost visibility. In contrast, European institutions face challenges in communicating their research effectively, limiting their impact and ability to attract funding. The U.S. communication model is characterized by personalized messaging, whereas European approaches tend to be more anonymized, raising questions about the role of branding and visibility in shaping public perception.

Despite the excitement around ChatGPT, explaining the complexities of AI systems remains a challenge. Misunderstandings persist, exacerbated by industry hype and unwarranted public fear. This imbalance could influence political decisions and investments, overshadowing genuine concerns about AI's limitations. There is growing recognition within the research community that AI systems are not infallible, yet tools to analyze their performance are still lacking. Issues like AI hallucinations are more visible than the nuanced aspects of its functionality, complicating effective communication.

### 11.3.2 The Role of AI Researchers in Building Trustworthiness

AI researchers play a vital role in promoting trustworthy and effective communication of AI, especially in fostering public understanding and informed decision-making. Effective communication skills are essential for researchers to convey the complexities of AI while avoiding exaggerated claims. Transparency is crucial in demystifying AI processes and clarifying the distinction between technologies that complement human labor and those that replace it.

Within the expert community, discussions often gravitate toward highly technical topics, sometimes overstating AI's capabilities. There is a pressing need for more public discourse about
what AI can realistically achieve, using clear and precise language to avoid creating unrealistic expectations that could influence policy decisions.

Researchers can counteract the "magic" perception of AI by providing clear explanations of their work and addressing misconceptions. While some companies may prefer to maintain a sense of mystery around AI, researchers can empower the public to make informed choices by explaining the underlying technologies. There is a fundamental responsibility for scientists to communicate their findings to the public, emphasizing the collaborative nature of scientific progress and managing expectations realistically [7]. Recent advancements in AI highlight the importance of addressing ethical considerations, which require open discussion.

To ensure trustworthy communication, researchers should be transparent about the sources and creation of AI products, challenge misleading claims, and raise awareness about ethical issues. However, effective communication cannot fall solely on the shoulders of researchers, as their demanding work requires support. Just as athletes rely on communicators in shaping their public image and increasing visibility, researchers can benefit from assistance for a quality communication.

Researchers in English-speaking regions often have greater visibility, while European institutions struggle with fragmented languages. A shift in communication strategies is necessary to amplify researchers' voices and enhance their visibility. AI should be recognized as a distinct discipline within the research field, with researchers providing historical context to explain its evolution and demystify its workings.

Researchers must remain committed to truth, navigating potential conflicts of interest and dispelling sensationalized claims. It is essential to clarify that AI models are based on prediction, not comprehension, similar to how doctors apply specialized knowledge. Transparent communication is crucial to managing public expectations about AI's capabilities and limitations.

Despite differing viewpoints within the AI community, researchers must maintain honesty and encourage nuanced understanding, bridging ideological divides to foster informed discussions.

### 11.3.3 Communication Strategies to Support Constructive Engagement with AI

AI communication strategies are complex, and researchers are not always aware of what specific strategies may apply to their work. One notable trend is the humanization of AI agents, making them more relatable to users. Involving people in testing AI is particularly valuable, as it provides tangible examples that are more easily understood and trusted by the public.

From their internal perspective, researchers report that certain scientists excel at explaining AI complexities, especially on social media platforms, where effective communication can mitigate the effects of hype. However, social media is often polarized, with users preferring opinions over complex facts. Platforms like YouTube are seen as more conducive to educational content, offering tutorials that help demystify AI processes.

Global analysis of science communication may help in understanding the dissemination of information across regions. Innovative methods like short, focused videos are viewed as effective in engaging younger audiences and promoting diverse perspectives. Ethical concerns in AI, particularly related to social media ownership and communication strategies, often capture public interest. Researchers also noted an increasing presence on television, offering a more balanced perspective on AI. Communication within research circles is dynamic, including personal blogs,
emails, and other mediums, with European institutions aiming to strengthen internal communication to better promote their research.

In academia, there is a growing emphasis on educating students about responsible AI use, with universities offering courses dedicated to ethical AI practices. Researchers and university press offices play a critical role in defining communication strategies, especially in addressing ethical issues around human-like AI interactions and embedding values in AI systems.

Simplifying complex AI concepts through memes and other accessible formats can help broaden public understanding. The integration of education and communication is evident, especially in making information accessible to different audiences, including children.

### 11.3.4 MUHAI as an Awareness Changer on the Role of Researchers as Communicators

The MUHAI project prompted young researchers, especially those in their early careers, to reconsider the role of communication in AI. Senior experts, however, expected only minor shifts, having already considered communication tasks in their project proposal. Researchers observed that MUHAI reinforced the idea of AI not as a replacement, but as a supportive tool ("generative AI"), reshaping perceptions of technological tools and their social impact. The project emphasized the importance of making AI more relatable to humans to foster complementarity, while acknowledging the risks in building trust in AI technologies.

MUHAI took place during a significant moment in AI history, with the emergence of language models like LLMs (Large Language Models) shifting the AI landscape. Researchers noted an increase in broader audience's interest, particularly as questions surrounding technologies like ChatGPT gained attention.

Researchers expressed that metrics often requested by the EU fail to capture the true impact of their work, and outreach efforts often feel like mere checkbox exercises rather than targeted engagement. Building a social media presence was seen as time-consuming and sometimes ineffective in reaching diverse audiences. However, collaborations across projects and interactions with various individuals continued to provide new perspectives.

One of the most significant impacts of the project was its engagement with the arts through exhibitions and collaborations, which researchers viewed as a powerful act. Art's ability to transcend traditional boundaries allowed for challenging feedback and broadened the political debate on AI.

### 11.3.5 The MUHAI Experience in Terms of Support from Communication Professionals

In the MUHAI project, communication professionals played a critical role by allocating time and fostering intensified interactions to reach broader audiences. This pressure to engage more fully helped expand the communication efforts. Communicators also played a vital role in framing questions, although there was a perceived imbalance in engagement among participants, with some not fully embracing communication activities.

Efforts to involve young and female researchers were appreciated but lacked depth. Communicators often assisted with revisions and refining papers, addressing the ongoing challenge of integrating communication into researchers' already heavy workloads.
In corporate settings like SONY, communicators are embedded within research teams, stream-
lining communication processes and sparing researchers from the burden of content creation
and campaign management. The complexity of AI research necessitates spending more time
on dissemination efforts, which researchers often struggle to balance with their primary re-
sponsibilities. Enlisting dedicated communicators to translate technical content into accessible
formats proves to be more effective.

Collaborative efforts between researchers and communicators are crucial, especially when en-
gaging with diverse audience segments. Researchers tend to focus on specialized channels like
Zenodo or ResearchGate, while communicators better understand public contexts. Training in
communication is beneficial for researchers, enabling them to craft compelling narratives and
adapt content for various audiences.

However, a gap remains between communicators’ knowledge and researchers’ expertise, pos-
ing challenges. Communicators must grasp AI’s various niches and innovative concepts to
effectively communicate research without oversimplifying. The EU projects emphasize social
media presence, but its impact is often limited due to project timelines. Traditional media
focuses more on results, while social media emphasizes processes and personal narratives. Live
events, such as Fairs, offer more meaningful interactions, allowing AI research to reach diverse
audiences.

11.4 Conclusion

The MUHAI communication team had the valuable opportunity to reflect on its activities and
expand this reflection to encompass the needs of AI researchers engaging in public debate. By
synthesising the insights gained from the interviews conducted and the literature reviewed,
we can now summarise and contextualise the key findings that have emerged. The Muhai
project, set during a transformative moment in the evolution of artificial intelligence (AI),
provided researchers with a unique opportunity to reflect on their roles in communication,
public discourse, and influence on policies. At a time when AI is being rapidly incorporated
and demanded across various sectors [12], the project prompted AI researchers to examine the
ways in which their work intersects with broader societal discussions. While the project explored
approaches distinct from those typically employed in Large Language Models (LLMs), as noted
in [1], researchers were acutely aware of the rapid pace of change in both AI’s application and
the public debate surrounding it.

Part of the research conducted during the project focused on understanding how users respond
to explanations about AI and the trust these explanations foster [16]. This study offered
valuable insights, mapping the views of both experienced and early-career researchers and
highlighting the complexities surrounding the communication of AI in society. A key finding is
that AI communication must adopt a transparent and balanced approach to foster trust among
the public and policymakers. Effectively bridging the gap between exaggerated promises and
unwarranted fears is essential for informed decision-making and the responsible integration of
AI technologies into society.

These insights align with recommendations made by UNESCO [4], which emphasize the need
for clear, balanced communication that avoids sensationalism while addressing societal con-
cerns related to AI. AI researchers acknowledge that they play a critical role in ensuring that
communication about AI is accurate, transparent, and accessible, thereby contributing to more
informed public discourse and the ethical deployment of AI technologies. This responsibility
involves disseminating knowledge about AI's functioning, enabling the public to make informed
decisions. However, researchers, particularly those at senior levels, often feel overwhelmed by
the demands of their research work, leaving them with limited time to devote to communication efforts. This makes the support of professional communicators vital in alleviating this burden. Additionally, providing training in science communication can enhance researchers' confidence and skills, making them more effective communicators to diverse audiences.

In terms of communication experiences, researchers generally express greater trust in traditional media, appreciating the moderated debates and expert perspectives they offer. While some researchers may have a deeper understanding of social media platforms and even find success in academic circles, there is widespread skepticism about social media's ability to facilitate informed discussions. Social media is often seen as a space where debate can become overly simplified, polarized, and superficial rather than conducive to building trust or fostering deep, nuanced knowledge exchange. In contrast, traditional media are expected to verify sources and present expert opinions, making them a more reliable platform for engaging with complex topics such as AI.

Regarding visual communication, MUHAI's efforts to challenge traditional representations of AI received positive feedback. Specifically, short, informative videos were viewed as a promising medium for providing insights into AI, particularly when these videos adopt a style that moves beyond the dichotomy of tech hype versus robot dystopia. What remains a quite open issue is to increase the access of a large audience of non experts to these materials. The use of exhibitions and art to tell the history of AI, for instance, demonstrates its potential to engage a broader audience and stimulate deeper discussions about the ethical implications of AI and its relationship with society.

From the perspective of the MUHAI project, the innovative visual approach and human-centric message continue to resonate in an era saturated with generative AI technologies. While the project did not dramatically alter public perceptions, it underscored the importance of robust communication efforts, especially for initiatives with significant communication resources. As AI paradigms evolve and renewed interest in symbolic AI emerges, there is a pressing need to redefine concepts of meaning and understanding in AI discourse. Projects like MUHAI can play an important role in raising awareness about the significance of researchers' involvement in communication and fostering broader public engagement with AI. They also offer valuable opportunities for training researchers in communication skills, enabling them to navigate complex public discussions.

From a science communication (scicomm) perspective, the research highlights the need for a collective effort to bridge the gap between researchers and communicators. This can be achieved through training, experience-sharing, and collaborative initiatives, which will ultimately enhance the dissemination of AI research and maximize its societal impact. Two main approaches emerge from the findings: first, the need for training in science communication for researchers, and second, the role of communicators as mentors, assisting researchers and promoting their work. Given the challenge of limited resources, particularly time, the second approach—supporting researchers with experienced communicators—was found to be the preferred option, as long as communicators possess sufficient understanding of AI concepts. Nonetheless, researchers also acknowledge the importance of being trained in communication to effectively interact with communicators and journalists, considering also the complexity of the topic.

This study aims to contribute to a better understanding of the role of researchers in presenting AI and highlights how science communication can support their work in dissemination and public awareness. By improving communication efforts and building stronger connections between researchers and communicators, AI research can more effectively engage the public, foster trust, and contribute to the responsible development and deployment of AI technologies.
Chapter 11: References

[FO1] Katrien Beuls and Paul Van Eecke. Humans learn language from situated communica-
tive interactions. what about machines? *Computational Linguistics*, 50(3):1277–1311,
December 2024.

[FO2] Philipp Brauner, Alexander Hick, Ralf Philipsen, and Martina Ziefle. What does the public think about artificial intelligence?—a criticality map to understand bias in the public perception of ai. *Frontiers in Computer Science*, 5, 2023.

[FO3] J. Scott Brennen, Philip N. Howard, and Rasmus Nielsen. An industry-led debate: how uk media cover artificial intelligence. 2018.

[FO4] Maarit Jaakkola (editor). *Reporting on artificial intelligence: a handbook for journalism educators*, volume 40 of *UNESCO series on journalism education*. UNESCO, Paris, France, 2023. Includes bibliography. Produced under the auspices of the World Journalism Education Council.

[FO5] Ethan Fast and Eric Horvitz. Long-term trends in the public perception of artificial intelligence. *Proceedings of the AAAI Conference on Artificial Intelligence*, 31(1), Feb. 2017.

[FO6] Anna Jobin, Marcello Ienca, and Effy Vayena. The global landscape of ai ethics guidelines. *Nature Machine Intelligence*, 1, 09 2019.

[FO7] Ilda Mannino, Laura Bell, Enrico Costa, Matteo Di Rosa, Alessandra Fornetti, Suzanne Franks, Claudia Iasillo, Neil Maiden, Arko Olesk, Jacopo Pasotti, et al. Supporting quality in science communication: insights from the quest project. *Journal of Science Communication*, 20(03), 2021.

[FO8] Elsir Ali Saad Mohamed, Murtada Elbashir Osman, and Badur Algasim Mohamed. The impact of artificial intelligence on social media content. *Journal of Social Sciences*, 20:12–16, Apr 2024.

[FO9] Julia Lynn Parra and Suparna Chatterjee. Social media and artificial intelligence: Critical conversations and where do we go from here? *Education Sciences*, 14(1), 2024.

[FO10] Roxana Radu. Steering the governance of artificial intelligence: national strategies in perspective. *Policy and Society*, 40(2):178–193, 05 2021.

[FO11] Roxana Radu. Steering the governance of artificial intelligence: national strategies in perspective. *Policy and Society*, 40(2):178–193, 05 2021.

[FO12] Adib Bin Rashid and MD Ashfakul Karim Kausik. Ai revolutionizing industries world-wide: A comprehensive overview of its diverse applications. *Hybrid Advances*, 7:100277, 2024.
[FO13] Anton M. Saveliev, Denis A. Zhurenkov, Artyom E. Poikin, and Tatiana A. Berkutova. Ethics of artificial intelligence and post-non-classical scientific rationality. *IFAC-PapersOnLine*, 54(13):397-401, 2021. 20th IFAC Conference on Technology, Culture, and International Stability TECIS 2021.

[FO14] Sne Scott Hansen. Public ai imaginaries: How the debate on artificial intelligence was covered in danish newspapers and magazines 1956-2021. *Nordicom Review*, 43(1):56-78, 2022.

[FO15] Andrea L. Guzman Seth C. Lewis and Thomas R. Schmidt. Automation, journalism, and human-machine communication: Rethinking roles and relationships of humans and machines in news. *Digital Journalism*, 7(4):409-427, 2019.

[FO16] Laura Spillner, Rachel Ringe, Robert Porzel, and Rainer Malaka. "my, my, how can I resist you?" - examining user reactions to bogus explanations of AI. In Marianna Bergamaschi Ganapini, Andrea Loreggia, Nicholas Mattei, Francesca Rossi, Biplav Srivastava, and Kristen Brent Venable, editors, *Proceedings of the Workshop on Ethics and Trust in Human-AI Collaboration: Socio-Technical Approaches (ETHAICS 2023) co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023) Macao, August 21, 2023.*, Macao, August 21, 2023, volume 3547 of *CEUR Workshop Proceedings*. CEUR-WS.org, 2023.

[FO17] Luc Steels. Conceptual foundations for human-centric AI. In Mohamed Chetouani, Virginia Dignum, Paul Lukowicz, and Carles Sierra, editors, *Human-Centered Artificial Intelligence (ACAI 2021)*, pages 8-35, Cham, Switzerland, 2023. Springer.

[FO18] Maurice Vergeer. Artificial intelligence in the dutch press: An analysis of topics and trends. *Communication Studies*, 71(3):373-392, 2020.
# Chapter 12

# MUHAI: The Path Forward

Luc Steels†,

† Venice International University, Italy

## Abstract

This final chapter summarizes the main lines of research of the MUHAI project. It surveys the objectives and achievements against the backdrop of recent rapid evolutions in AI. It also sketches possible ways to go forward

## 12.1 Human-centric AI

The MUHAI project was conceived in the spring of 2019. The proposal was submitted towards the end of 2019 and the project started in October 2020 to run until March 2024.

In 2019, deep learning was already around, although the first large language models would only appear on the scene in 2022 when Open AI launched ChatGPT. In 2019, the limitations for using neural models beyond the task for which they were trained (namely prediction) were also already known: statistical induction and generation are prone to hallucinate unjustified inferences. They lack human-understandable explanation capacities, and deceive their users. These observations had triggered calls for a *human-centric form of AI* to complement the data-driven neural approaches that were beginning to stream like a tsunami over AI research and its applications.

Human-centric AI is considered to require the following features [12]:

* Systems are required to be aware of the *goals and intentions* of their users and base their own goals and dialog on *meanings* rather than statistical patterns of past behavior, although still acknowledging that statistical patterns can play a very important role, for example, to drastically reduce search or carry out approximate inference.

* Human goals and values should always take precedence. Respect for human autonomy should be built into the system by design, leading to qualities such as *fairness and respect*.

* Human-centric AI systems are asked to be able to explain their reasoning and learning strategies so that the *decisions are understandable by humans*. Only by emphasizing human understandability will human-centric AI achieve proper *explainability and transparency*.
* Human-centric AI systems should not only learn by observation or theorizing about reality but also by *taking advice* from humans, as suggested in John McCarthy's original 1958 proposal of the Advice Taker.

* Human-centric AI should be able to use *natural communication*, i.e. communication primarily based on human language, not only by mimicking language syntax but, more importantly, using the rich semantics of natural languages, augmented with multi-modal communication channels. This is needed to support explainability, and accountability.

* Human-centric AI systems should have the capacity of *self-reflection* which can be achieved by a meta-level architecture that is able to track decision-making and intervene by catching failures and repairing them. By extension, the architecture should support the construction of a theory of mind of other agents, i.e. how they see the world, what their motivations and intentions are, and what knowledge they are using or lacking. Only through this capacity can AI achieve intelligent cooperation, adequate explanations, and efficient learning through cultural transmission.

* Finally, human-centric AI systems should reflect the *ethical and moral standards* that are also expected from humans or organisations in our society, particularly for supporting tasks that are close to human activity and interest.

All of these objectives point in the direction of meaningful AI, i.e. AI where meaningful distinctions are used as conceptual building blocks for rich models of problem situations and where deliberative reasoning complements reactive behavior.

The desired properties of human-centric AI are all very difficult to achieve and certainly far beyond the state of the art. The goal of the MUHAI project is to advance in this direction.

## 12.2 Key hypotheses

The first hypothesis underlying the MUHAI project is that to be human-centric, AI must cope to grips with meaning and understanding. Meanings are distinctions (categories) that are relevant for prediction, classification, communication, problem solving, or other mental tasks. The meanings of an experience include what events, actors and entities play a role, the temporal, spatial and causal relations between events, intentions and motivations of the actors, and values that implicitly underlie their behavior. Understanding is the process of reconstructing these meanings and organizing them in terms of a coherent narrative that explains a new experience by linking it to a personal dynamic memory.

The second hypothesis is that this reconstruction process requires the use of multiple knowledge sources, as illustrated in Figure 12.1.

On the one hand, text, images, sound, actions flow as input into a system, depending on the application domain. On the other hand, rich models are provided as output and flow back into the understanding process. Here are some of the knowledge sources that the MUHAI project has focused on. It was necessary at the time of the proposal to push further the state of the art for each of them and it is still important work for the future.

* *Language processing tools* that transform text into morphological lexical, syntactic, semantic and pragmatic structures.

* *Ontologies* that provide inventories of concepts to be used for constructing semantic and pragmatic structures and symbolic models of what the input sources are about.
<figure><img src="image_51.png" /><figcaption>Figure 12.1: Understanding calls upon a variety of knowledge sources and uses itself the partially constructed rich model. The knowledge sources use both reactive and deliberative AI.</figcaption></figure>

* *Knowledge graphs* that provide general facts about the domain to be used in inferencing or language analysis.

* *Discourse models* that are necessary in the case of dialogs to keep track of the interlocutors and their attitudes and background knowledge.

* *Vision and pattern recognition tools* that transform visual or audio input streams into features and interpretable structures.

* *Mental simulation* that can be used to reconstruct the past or predict the future.

* *Episodic memory* contains a repository of past experiences that can be used to interpret new ones or infer more information about them.

* *Action monitoring* is concerned with following up whether actions having been planned are executed according to this plan.

The third hypothesis of the project is that we need to consider the models made as part of human understanding to function as *narratives*. A narrative is a way to structure experiences.[1], [19] It identifies the relevant events connected principally by temporal and causal links, further enhanced with spatial, logical, analogical, hierarchical and other relations. A narrative identifies the actors and entities and the roles they play in events and the relevant properties of the events. It includes insights into the broader context and the motivations, deliberations and intentions of the actors.

The more a domain is related to human issues, the more narratives also include a moral viewpoint on the events and an ideological framing.[6] So a narrative combines a set of seemingly disconnected facts and episodes into a coherent structure in which the relationships between events and facts, the relevance of the events, as well as their moral and ethical framings are made explicit in order to explain and justify actions or convince others of a particular viewpoint.

*Narrative intelligence* is the ability to come up with narratives, either based on observations of reality or on semiotic representations of narratives (texts, novels, drawings, movies) created by others. Narrative intelligence exploits general facts in *semantic memory* to fill in details, sensory-motor data stored in *episodic-procedural memory* to ground narratives into reality, *mental simulation* to imagine how situations in the world will unfold visually, and memory of past narratives, often called *narrative memory* - or *autobiographic or personal memory* if the
narratives are about making sense of your personal life. In human intelligence these different
types of memory are personal, based on your history of interactions with the world and others.
Today knowledge graphs and other semantic resources are assumed to be universal, but we
must expect that future meaningful AI systems have their own ‘personal’ dynamic memories
which they have acquired through their own interactions with the world and other agents. They
will undoubtedly have their own opinions or ways of framing reality that may differ from that of
other AI systems or from humans.[18]

The human propensity for creating narratives and adopting, modifying and communicating them as narrations is particularly well illustrated with the discourse on the Covid pandemic and vaccination campaigns. Because there is a general lack of understanding and an ongoing change in the nature of the covid virus, we see that scientific theories need to adapt constantly to new insights, observations, the behavior of variants and the behavior of populations (keeping distance, masks, etc.). But we also see a wealth of spontaneous semi-fictional narratives, some of them taking the form of conspiracy theories or fake news stories, narrated through memes on social media, that are actually harmful for those believing them and hamper gaining collective control over the pandemic.

There was already a flurry of research activity in AI on narratives between the mid nineteen-sixties and late nineteen-seventies. One of the first ideas that came up is the notion of a *semantic network*, as first proposed by Quillian[13], which blossomed into the knowledge graphs that we have today. Another notion is that of a *schema* or *frame*, as first proposed by Minsky [11], or *script*, as proposed and worked out by Schank and colleagues [17]. A frame is a set of questions to be asked about a particular situation with constraints, defaults and strategies to find answers. The classical example is a restaurant frame that structures the experience of entering a restaurant.

Schank also triggered the first AI research into dynamic semantic and episodic memory and how they play a crucial role in understanding[16]. These ideas developed further in memory-based or case-based AI applications.[7] Frames and scripts have underpinned a large number of earlier experimental AI systems for story understanding and story generation and for expert systems not based on rules but on solving cases by analogy with earlier cases. They also lead to some remarkable early demonstrations of language understanding capacities in the 1970s, such as Winograd's SHRDLU system, dubbed a system that could understand natural language,[20] and the HEARSAY speech understanding system that worked on spoken language.[14] Later in the 1980s we also saw the first attempts to build encyclopedic knowledge bases, of which CYC [8] was the most important representative.

Interest in narratives waned somewhat in the late 1990s as the attention of AI shifted to behavior-based robotics, neural networks, and the semantic web. Moreover it was realized that incorporating narratives as the core of AI was going to be a daunting task. The broad humanistic scope that characterized AI in its first four decades began to shrink with a focus on narrowly defined problems with measurable performance under economic pressures. But the past decade we have seen renewed attention in AI research to narratives [9], [21], [4], [15], [5], [10]. Efforts now incorporate also data-driven AI methods to learn aspects of narrative intelligence, such as the reconstruction of timelines or characters.[? ]. Still, it is early days and many many problems remain unsolved.

The fourth hypothesis concerns the process with which narrative models are built up. Many different knowledge sources contribute to the build up of a narrative network and we certainly need to go beyond a strict pipeline model that dominates data-driven AI, towards a flexible architecture in which different knowledge sources can contribute at any time, either because new information has become available, so that a knowledge source can make a useful contribution,
or because they are called in a top-down manner to expand areas of the narrative network. In past AI work, this kind of flexibility was often achieved with blackboard architectures based on the metaphor of a blackboard on which various knowledge sources can read and write.[3]

In the case of understanding, the blackboard contains the transient narrative network. The knowledge sources include sensory input, language input, mental simulation and semantic and episodic memory. Blackboard architectures have resonated with models of consciousness in neuroscience, specifically the Global Neural Workspace Model by Bernard Baars and refined by Stanislas DeHaene and colleagues.[2]

A blackboard-like architecture is a first step but we will need much more:

* First of all, we need ways in which the understanding system can *measure progress*: (i) how far ambiguity, incompleteness, underspecification and incompleteness is being reduced, (ii) how information has become integrated, meaning in how far network fragments could be connected to each other and how connections between different levels of the narrative (the fabula, story, and narration) could be established, and (iii) how far the distance towards a satisfactory narrative closure could be bridged. Narrative closure happens when the key questions for which the narrative is being developed have been answered.

* Second, these measures should be input to an *attention mechanism* that decides where further effort should go: Should more resources be applied for analyzing and interpretating certain inputs? Which nodes in the network are to be expanded preferentially? What additional inputs might profitably be sought? Should a knowledge graph be consulted? How many extra resources should be put into visual processing? It is unavoidable that the degree of understanding of a narration will sometimes decrease because new input enters that is not compatible with what was seen before or new facts are supplied by semantic memory that create a cognitive dissonance with the narrative network built so far.

* Third, there needs to be a *meta-level* that plays three crucial roles: (a) It should monitor progress and avoid catastrophic failure by catching fail states in components (in the same way an operating system catches an error in running applications and possibly repairs them to avoid that the whole system collapses). (b) It should govern learning processes, such as the process by which a narrative is stored in episodic memory to deal with similar situations in the future. (c) It should include a value system that monitors decision-making (and subsequent real-world action) to make it compatible with a moral framework that is compatible with human values in order to realize a form of *value-aware AI*.

The MUHAI project has pushed forward research on each of these four hypotheses. It has in particular made more progress on meaning-oriented language processing using the Fluid Construction Grammar framework, on mental simulation related to every day objects, on exploiting network theory for mapping out semantic landscapes, on tools to represent narratives and on measuring progress in reaching a coherent understanding of a situation.

## 12.3 Applications

It is common practice in AI to develop and test ideas, mechanisms, and their implementations by tackling concrete domains and focus on the realization of specific applications on which different partners can work together. Building applications requires of course a lot of work beyond the specific scientific aims of the project, but it is also the way to
demonstrate research achievements for a non-technical audience.

The domains tackled by MUHAI fall broadly in two classes: The domain of every day activities in the real world and the domain of social relations and behaviors in society. Whereas Volume 1 in the series contained more general background discussions, Volume 2 focused on the first application domain and volume 3 on the second one.

### 12.3.1 The Cooking Domain

For the domain of every day activities, the project has selected to work on cooking and more concretely on understanding cooking recipes. Understanding means here to extract from a recipe enough information to effectively be able to cook the dish described by the recipe. And this requires more than language processing. It also requires dealing with common sense knowledge, meaning-form mappings, and mental simulation.

It turns out that understanding recipes is hard for a number of reasons. Part of the problem is to figure out what items and actions the descriptions in the recipe refer to, which objects are involved in each action, and what the parameters of actions are, for example how long something needs to be cooked. Often you also need to come up with alternative solutions to reach the same (implicit) goals.

Next to these grounding problems, there are problems with ambiguity and underspecification. Consider a typical example of a line in a recipe: 'Place the lentils in a pan and add enough water to cover them with a layer of 5 centimeters.' Some of the words in this sentence are ambiguous. For example 'pan' means here a cooking pan but the word can also refer to the action of taking a wider view with a camera. 'Cover' means here 'to put something over something else' but it can also mean 'the object with which you cover something', 'to deal with a subject', 'the outside of a book', 'a place to go hiding'.

Next there is the problem of underspecification. The line talks about 'a pan' assuming there is such an object available in the context. 'Them' is presumably refering to the lentils. The water has to be added but to what - presumably to the pan. The recipe does not specify the exact quantity of water to be added but describes instead the end state ('until there is a layer of 5 cm').

Clearly, a recipe is not like a computer program which is fully determinate and unambiguous. Understanding the recipe so that you can cook the dish, requires disambiguating, contextualizing and grounding the objects mentioned in order to resolve ambiguities and handle underspecification and incompleteness as much as needed. Doing this requires a significant amount of background knowledge but also the ability to build models of reality based on sensori-motor input, perform mental simulation of actions, and link the referents of descriptions in the recipe to the model.

The cooking domain is representative of a much larger set of domains with similar properties such as instructions for putting together IKEA furniture, manuals for installing and operating equipment, office procedures, legal procedures, instructions for a field trip in the forest.

### 12.3.2 The Societal Domain

When studying examples from economics, sociology, history, demography, medicine, social neuroscience, and other human-oriented and social disciplines we see that the rich models constructed to understand situations all take the form of narratives. Although these
narratives may not be 'true' in a purely fact-based sense, they nevertheless can strongly
influence opinions and consequently real world actions. We also see that there is variation
in a population with respect to narratives made about the same situation and this is one
of the main reasons why conflicts arise, including violent conflicts.

Recent developments in digital media, in particular the rise of social media, has given unprecedented access to how narratives develop and spread in human populations and MUHAI has developed tools to reconstruct such narratives from analysis of social media and other sources reflecting opinion dynamics.

More concretely the MUHAI project has engaged in a number of concrete case studies for learning about society through analysis of social media data, demographic data, and art works, more concretely the following social topics have been addressed:

- Violent conflicts arise often because of attempts to seize resources from another party. In order to get acceptance, these conflicts are explained to the population in terms of narratives that introduce an us versus them dichotomy, a de-humanisation of the opponent, and also incomensurable interpretations of acts of violence. This is all clearly illustrated in the war of aggression of Russia against the Ukraine, which is in Russia seen or framed as an aggression against them.

- In the domain of medicine there are not only scientific theories and narratives based on them, but there are at the moment also a large number of so called 'common sense' narratives. A good example are the anti-vax narratives that emerged in relation to Covid. The point is that such non-scientifically grounded narratives can gain considerable traction in a population causing infection diseases to propagate faster and deeper than if the scientific consensus would be followed.

- Inequality is another domain in which narratives play a significant role because they shape policy and the perception of people have of their role in society. Also in this domain MUHAI conducted case studies, particularly on opinion about the possible causes of inequality and the relation to other social factors.

In order to deal with these case studies, the project has relied on many existing techniques of AI ranging from deep learning networks, and language processing to knowledge graphs, but also pushed their boundaries and developed new techniques all operating in the service of giving AI systems a better grip on meaning and thus on explanation and other key issues for achieving human-centric AI.

12.4 Looking into the future

Human-centric AI based on meaning and understanding is certainly not a reality although progress has been made (also outside of the MUHAI project).

On the positive side, some of the gaps in terms of available knowledge sources have been plugged to a significant extent. This is for example the case for mental simulation. The computational resources needed (GPUs) and the available real world models for mental simulation have considerably advanced primarily pushed by the development of computer games. Another example are knowledge graphs. By now the technology underlying knowledge graphs has been scaled so as to support very large graphs and a wealth of ontologies and knowledge repositories have been constructed and become openly available. Many of these knowledge graphs have been incorporated in the case studies carried out by MUHAI.
On the negative side, linguistic language processing, i.e. language processing based on grammatical and lexical models that map meaning to form and back, has not progressed significantly, partly because the attention of the NLP and computational linguistics community has been geared towards the development of large language models. Although tools, such as the Fluid Construct Grammar toolkit developed in the MUHAI project, make it now possible to build broad coverage grammars, the linguistic community has so far not come into action to use these tools for building realistic grammars. Building broad coverate grammars requires considerable human resources and expertise in linguistics but the cost will still be much smaller than currently being used to build large language models. Similarly, large scale inference for common sense domains (for example reasoning about liquids) has not been developed to the extent needed to effectively tackle a domain such as cooking. Here also it is not a matter of computational resources anymore but rather of gathering and axiomatising the knowledge needed.

These are needed directions for future research. But probably the most important change lies in integrating large language models (including multi-modal models) and neural-based pattern recognition processes in understanding systems. Large language models can have various functions:

- They can support the vision system to gain situation awareness and aid in speech input/output.

- They can act as an episodic memory, memorizing action sequences obtained by deep language analysis and inference, and use these memories for quick reactivity from then on. This will require advances however in neural network learning so that these learning systems become incremental.

- LLMs can furthermore complement the role of mental simulation by doing approximate predictions of action sequences, which can then be analysed and carried out.

- Even though they cannot do accurate meaning-oriented language processing, LLMs can help to decrease combinatorial search during parsing and constrain the syntactic categories and semantic properties of unknown words.

The advantages go both ways. Coupling understanding with the use of LLMs would enhance several knowledge sources but it would also enable LLMs to cope with known limitations, such as hallucinations, dealing with outliers, and explainability.

Finally, a major topic that could not sufficiently be tackled by the project, mainly because the knowledge sources did not reach enough maturity, is the coordination of different knowledge sources to reach an integrated interpretation. Some experiments have been conducted (for example, the interpretation of art works that bring together vision, language, and knowledge graphs) but much more needs to be done.
Chapter 12: References

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