ISSN 2186-7437

NII Shonan Meeting Report

No. 200

Social Explainable AI:
Designing multimodal and interactive
communication to tailor human–AI
collaborations
Kary Främling
Brian Y. Lim
Katharina J. Rohlfing
September 18–21, 2023

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan

Social Explainable AI: Designing multimodal andinteractive communication to tailor human–AI
collaborations
Organizers:
Kary Främling
Department of Computing Science, Umeå Universitet, Sweden
Brian Y. Lim
National University of Singapore, Singapore
Katharina J. Rohlfing
Transregional Research Center Constructing Explainability,
Paderborn University, Germany
September 18–21, 2023

Abstract
In recent years, research on explainable AI (henceforth, XAI) has intensified,
responding to the societal challenge that many algorithmic approaches (such asmachine learning or autonomous intelligent systems) are rapidly increasing incomplexity, making it difficult for citizens to understand their assistance and toaccept the decisions they suggest. There now exists a large body of approaches
pushing forward many ideas of how algorithms should be made explainable oreven be able to explain their own output. However, the users’ perspective israrely taken into account seriously. Obviously, not only research dimensions but
also putting into operation a true involvement of the users are missing.
In this meeting, we therefore gathered scholars from different disciplines toaccount for the question of how explanation generation can be tailored to theusers and their way of understanding. Starting this 4-days meeting, we proposed
that, rather than being ‘delivered’ by the explainer, explanations become tailoredwhen they emerge at the interface between the explainer (or explaining system)on the one hand and explainee (the receiver of an explanation) on the other hand.
Both are active participants in shaping explanations during a social interaction.
We thus considered social interaction to be the key to the involvement of the users.
Derived from the state of the art, we structured our meeting along three facts
concerning social interaction in general and explanatory dialogues in particularthat became the structuring dimensions of our approach:• Interaction is multimodal (e.g., visual, verbal, auditory), so XAI needs toaccount for different modalities of communications that are used in theprocess of constructing an explanation.

1• Interaction is incremental and builds on the contribution of the involved
partners who adapt to each other. In this sense, an explainee can also
contribute to a successful explanation, e.g., by asking questions or byproviding feedback regarding their understanding.
• Interaction is patterned in the sense that different contexts and goals willlead to the emergence of different social roles impacting the construction of
explanations. In the case of an explanation, specific conversational patterns
are followed, e.g., patterns of explicating the relations.
We achieved to agree on a conception of social XAI that is based on researchin social interaction, and we raised awareness for this perspective among theparticipating scholars. We worked out a structure of a handbook for social XAI
that will be published to keep the emerging community engaged. With the
handbook, it is our objective to extend current research in computer science andoffer new answers to the abovementioned societal challenge.

2

Background and Introduction
In our digitized society, computational approaches (such as machine learning or
autonomous intelligent systems) are rapidly increasing in complexity, making it
difficult for citizens to understand their assistance and to accept the decisions
they suggest. In response to this societal challenge, research on explainable
AI (or XAI) has intensified, pushing forward many ideas of how algorithms
should be explainable or even be able to explain their own output. Consequently,
recent work on XAI has broadened its perspective, tackling topics such as verbal
explanations, interactivity of systems, hybrid approaches combining reasoning
and learning for XAI, and the relevance of explanations to the users.
Whereas the emerging XAI approaches are concerned with interpretability or
explainability, more recently, state-of-the-art research reveals a lack of contextawareness [1], a lack of interaction as well as personalization as reasons for why
an explainable system is of little use to the users [2]. Responding to this gap in
more details, current reviews define dimensions of which to consider the users’
perspective, but also identify a gap of approaches toward the involvement ofusers. Clearly, an explanation addressing the why does not necessarily lead toan understanding. Instead, the interaction of current XAI systems is severely
limited because they can only deliver an explanation, without tailoring it to
the receivers’ understanding and informational needs, nor to the given context.
Responding to this limitation, Miller [3] argues that XAI can benefit from social
science research. In this line of research, social interaction seems to be the key formaking an explanation provided by a system understandable and relevant. The
big advantage of viewing social XAI as socially interactive explainable AI is that
concrete operationalizations for how to involve the users can be derived fromresearch on social interaction. This is indicated in [4] proposing a framework
for a social design of XAI. For this, the properties of an interaction need tobe considered. These properties go beyond what has been suggested by Miller
viewing explanations as selective and contrastive to foster the cognitive process of
abductive reasoning (i.e., deriving a hypothesis to explain observed phenomenon).
In fact, Brasse and colleagues detect that mainly cognitive theories are employedin XAI [5] that limit the current perspective. To solidify theoretical foundations,a broader and interaction-oriented perspective is needed.
To structure our view along clear dimensions and add to characteristics
of an explanation proposed before, we focused on the following properties ofinteraction:• multimodality (e.g., visual, verbal, auditory), so XAI needs to accountfor different modalities of communications that are used in the process ofconstructing an explanation.
• incrementality, which means that an interaction consists of elementsthat follow on each other allowing to build on the contribution of theinvolved partners who adapt to each other. In this sense, an explainee canalso contribute to a successful explanation (e.g., by asking questions or byproviding feedback regarding their understanding).
• patternedness in the sense that different contexts and goals will leadto the emergence of different social roles impacting the construction of
explanations. In the case of an explanation, specific conversational patterns
3are followed that also give rise to cognitive patterns (e.g., relations about
the observed phenomenon will be explicated yielding cognitive operations
that are typical for explanations and abductive reasoning).
Whereas in our discussions during the meeting, we agreed on a conceptionof social XAI that is based on research in social interaction and raised theawareness for this perspective within the participating scholars, our aim was togo beyond our meeting and to foster the emerging community in a sustainable
way. Therefore, we worked out a structure of a handbook for social XAI thatwill be published to keep the emerging community engaged. With the handbook,
it is our objective to extend current research in computer science and offer
new answers to the abovementioned societal challenge by contributing to thedevelopment of:• a multidisciplinary understanding of the mechanisms involved in the process
of explaining, tailoring it to the process of understanding,• computational models and complex AI systems that focus efficiently on
what kind of explanation a person requires in a current context, and by• multimodal interactions to achieve an interaction that unfolds over time andthat makes a joint construction of relevant explanation patterns possible.

4Overview of Talks
What’s New in Social Aspects of XAI?—Current Research
Gaps
Katharina J. Rohlfing, Transregional Research Center Constructing Explainability,
Paderborn University
For our meeting, we started with the definition of artificial intelligence (AI)as intelligent systems that interpret data, learn from it, and use this learning toachieve specific goals and tasks [6]. According to the literature (e.g., [7]), weak
and strong AI systems can be differentiated: Whereas weak AI is developed forspecific tasks, strong AI is considered to be more flexible. The black-box natureof these models or their complexity is opaque not only to users but also to theirdevelopers. The models bear risks of biases and deskilling [7] that are based on
system’s (e.g., based on correlations) or human’s behavior (e.g., overreliance).
The XAI is viewed as the solution to these problems. XAI is referring to“many user-centered, innovative algorithm visualizations, interfaces and toolkits”
that support users with various levels of AI literacy to enable them to understandand trust [8, p. 1]. In fact, explainability is considered a prerequisite for fair,accountable, and trustworthy AI, eventually affecting how we manage, use, andinteract with it [7]. XAI is applied in “diverse subject domains, from the bankcustomer who is refused a loan, the doctor making a diagnosis with a decisionaid, to the patient who learns that he may have skin cancer from a smartphone
photograph of his mole” [8].
Whereas XAI is viewed as solution to the problem of opacity, it has todate been mostly investigated with a method-oriented focus for developers incomputer science [7]. In his seminal work, summarizing existing research on XAI,
Miller [3] called for explanations that have to become more social in order to be
relevant and helpful. Since then, research has turned to this objective attempting
to address this call.
In this introductory talk, a summary of six recent overview articles was given
to the participants. This way, a ground was provided for discussions about the
specification of the term “social XAI” and what social aspects are needed in XAI.
• The paper by Meske and colleagues [7] presents the process of decisionmaking in which an XAI is involved. The innovation of the paper is toemphasize the management of organizational processes as a goal of
XAI. It is based on the argument that XAI as a new form of material agency
in organizational processes changes work routines. Groups of stakeholders
are identified that should be regarded for the new goal of the XAI (Figure
3 on p. 58): AI regulators, AI developers, AI manager, AI users, andindividuals affected.
• From the beginning, the paper by Ali and colleagues [9] differentiates
between explainability of either the data, model, or post-hoc statements(Table 2 on p. 10). With this differentiated view, it provides an extensive
overview on the variety of methods that are available in XAI criticizing thatmany methods already exist and can elicit explanations of various kinds.
However, the methods achieve it without regard to whether they satisfy
the intended audience’s needs [9] as can be seen by the authors’ analysis of5the methods presented in dependence on the users and their satisfaction.
This way of presentation is derived from the original goal of XAI researchwhich is to make AI systems more comprehensible and transparent tohumans without sacrificing performance. The goal is reached when humans
understand and trust the AI solutions. In Figure 27 (p. 32), the authors
summarize the assessment methods currently available. The purpose of thisextensive presentation is to define how to attain trustworthy AI. In
Figure 29 on page 39, this paper also provides a landscape of the decisionmaking process that XAI supports. This landscape informs about whatgroups of users need to be taken into account. Whereas this paper manages
to propose that trustworthiness is about explainability and how to assess
it within a whole decision-making process, it does not go beyond pointing
out that the receiver of an explanation has to be taken into account more.
• The paper by Brasse and colleagues [5] provides an analysis of theapplication scenarios that are considered in current XAI research.
It successes in communicating to what extent different types of users(developers, experts, or lay users) are taken into account and in what kindof scenarios. Interestingly, the domains of healthcare and finance seemto be predominant in most of the XAI-studies. From their analysis, theauthors conclude that XAI is “not very theory-rich” [5, p. 25] and mainly
cognitive theories are employed. Clearly, there is a gap on social aspects of
explanations and their long-term influence.
• The title of the paper by Haque and colleagues [10] promises to take theuser’s perspective. Indeed, the paper is about understandability and it
departs from the XAI goal to empower users to adopt AI-based systems.
The authors focus on users’ mental models including their beliefs
and perception about the external world. It suggests that users canbe grouped accordingly when developing an XAI for specific purposes. Thisapproach, however, results in a static XAI that is designed for a target
group but is not able to fine-tune or modify its predefined actions during
an interaction. In their suggestions for future research, the authors raiseinteresting novel questions such as what is the impact of AI on low-literate
persons.
• In their paper, Arrieta and colleagues [11] provide concepts and taxonomies
that guide toward responsible AI with the goal to place audience as a key
aspect to be considered when explaining a machine learning model. Thisway, they address the research gap to involve the user’s perceptive. For
their purpose, in Figure 2 on p. 83, they not only identify different groupsof the target audience (domain experts, regulatory entities, managers,
data scientists, developers, product owner, and users affected by model’s
decision) but also define different levels of transparency, on which basis
they develop their principles of responsible AI. Accordingly, a responsible
AI must not discriminate (fair AI), take audience into account (transparent
AI), generate benefit (human-centric AI) and consider privacy and security.
This focus on responsible AI is clearly advancing current approaches but
the question of how the audience can be taken into account remains
unanswered.

6• Providing some concrete answers to the question of how the audience or auser can be taken into account, the paper by Chromik and Butz [12] offers
design principles for explanation user interfaces. Specifically, it takes asystematic look at the way the interaction is designed in the XAI
research. For their purpose, the authors define explanation user interface(XUI) as “the sum of outputs of an XAI system that the user can directlyinteract with”. With this definition, the explanatory power is assigned tothe interaction and not to a statement. The paper introduces a taxonomy
of interaction, according to which the applied XUI can be categorized.
The taxonomy corresponds to metaphors with which interaction can beviewed as: information transmission, dialogue, control, experience, behavior
optimization, tool use or embodied action (see Table 2 on p. 14 for an
overview). The different categories of the interaction differ with respect of
the system’s interactivity, i.e., how much the user can be involved in theinteraction and influence the system’s output. The authors propose that fora system to be interactive, it needs to take advantage of the naturalness ofa human–human dialogue, its incrementality allowing follow-up on initial
explanations, flexibility through multiple ways to explain, and sensitivity
to the user’s mind and context of explanation. The gain of the paper lies in
concrete suggestions on how a user’s involvement can be achieved and how
to operationalize it. It reveals, however, that there exist little approaches
allowing preemptive task co-management and shared progress tracking ina human–XAI interaction, which abilities make a system flexible and allow
an explanation to be tailored to the user.
In summary and following the conclusions by Ali and colleagues [9], mostresearch on how to interpret and explain AI systems is mainly motivated byrequirements of the developers rather than users. However, to comprehend an
AI system satisfactorily, each user needs a different level of explanation [2].
For these levels, many solutions were proposed [9, 10, 11], however they rather
categorize the users as a group that has particular characteristics and needs
that require a specific way of explaining. More flexible systems are still lacking
and barely respond to the original call for explanations in which the explainerand explainee interact with each other [3]. This vivid process, in which bothpartners contribute to the explaining, was recently described in details in natural
everyday explanations [13].
It can thus be concluded that whereas social aspects are well-recognizedin current XAI research and partially identified, what is missing is a stronger
guidance toward how to involve users in a meaningful social interaction in order tomake an explanation relevant and tailored to the user’s emerging understanding.
For this meeting, it is proposed that what is lacking are the system’s abilities
to be multimodal, incremental, and patterned. These necessary properties were
further described in the invited talks below.

Multimodal Interaction Shapes Explanation Dialogues
Angela Grimminger, Paderborn University
Hendrik Buschmeier, Bielefeld University
This talk addressed the multimodality of human interactions and how thisis important for the co-construction of dialogical explanations. In this talk,7

multimodality was defined as the different forms of verbal and non-verbal signalsthat interlocutors use during communicative interactions. Social interaction
naturally takes place ’face to face’ and interlocutors produce and perceive acontinuous stream of meaningful bodily behaviour [14]. The modalities used insocial interactions can be vocal/aural (including speech and prosodic features)or visuospatial (including hand gestures, head gestures, facial expressions, body
postures, actions and gaze) [15, 16]. All of these behaviours in human interaction
can be produced or perceived, for example, as signals of attention [17], for turnmanagement [18], or as feedback [19, 20] and thus shape the ongoing interactions.
Various multimodal means and their contribution can already be observed in
the earliest parent-child interactions [21, 22, 23]. Using a video example of a
dialogical explanation of a board game, the talk showed how non-verbal andmultimodal forms of feedback from an explainee signal both understanding andmisunderstanding, and how these signals can influence the explainer’s ongoing
explanation.

Interaction Is Incremental
Kary Främling, Umeå University
The human brain is presumably the least well understood "black-box model"
on Earth. Despite this, humans are capable of introspection and self-reflectionthat allows them to justify and explain their reasoning and decision-making invarious ways, depending on the explainee, the context of the explanation, andother factors. Human explainability is a social interaction whose objective isoften to convince the explainee about the rationality of the explainer’s reasoning.
Current state-of-the-art eXplainable AI (XAI) methods lack such interactivity.
Even though it might be possible to divide the explanation into smaller chunks,
the mathematical limitations of the XAI methods may prevent them from taking
into consideration interactions between features and to misleading explanations
where feature influences cancel each other when combined into feature coalitions.
The Contextual Importance and Utility (CIU) method [24, 25] overcomesboth of these challenges, as well as other challenges of current state-of-the-art
XAI methods. The presentation shows how CIU can answer questions suchas “Why?”, “Why not?”, “Why A and not B?” (contrastive) and "What if?"(counterfactual) using a human dialog for justifying the choice of a car as
an example. CIU’s "intermediate concepts” deal with feature interactions incoalitions of features in a mathematically consistent way. CIU also avoids thephenomenon of feature influences cancelling each other due to the use of classicaldefinitions of “importance” and “utility” from Decision Theory, which are extended
to deal with non-linear models such as those represented by neural networks andother machine learning methods [26].
Finally, the emphasis of the presentation is to advocate that XAI researchshould focus on what kind of questions humans tend to ask and how theunderstanding of the black-box model’s reasoning can be built through anincremental interaction process. CIU provides building blocks to bridge the gap
between such Social AI and XAI methods [27]. However, achieving a truly Social
AI will require an extensive collaboration effort between different domains suchas sociologists, psychologists, human-computer interaction specialists, cognitive
scientists and computer scientists.
8

Interaction Is Patterned
Anna-Lisa Vollmer, Bielefeld University
In his qualitative studies of parent-child interactions, the US-American
psychologist Jerome Bruner identified interaction patterns that he called interaction
formats [28]. He identified game formats like peek-a-boo, and the nursery rhymes
ride-a-cock-horse that follow a relatively fixed structure in interaction patterns,
utterance choice, and prosody. He also observed such game patterns that emerged
between child and caregiver and as such were individual for this dyad. Bruner
found picture book-reading to also happen in a format. Formats have a variant
surface structure that include the concrete observable utterances and behaviorsof the interaction partners (Emma, look! with pointing | Looking| What’s that?
| Babble | Yes, a pineapple! | an apple | yes), and an invariant deep structurethat pertains to the abstract interaction structure (attentional vocative | query |correct label | feedback). [29] have proposed the concept of pragmatic frames (PFs)for learning words. The PFs are a sequence of coordinated behaviors – actions
and language – of both interaction partners. PFs are recurrent. They emerge overtime and first occur in a specific context. They also link the surface behaviors
to the underlying cognitive operations (locate and follow finger | segment andrecognize object | recognize name | link object with name). The relatively fixedpatterns, once established over repeated interactions, carry meaning and help thelearner to pick up the only variable information – in the book-reading frame theobject label – that the learner is supposed to learn. The roles in the frame can beswapped such that the caregiver can take over the role of the learner when they
are not able to fully participate in the frame yet. In human–robot interaction,
fixed interaction patterns or protocols are used in learning from human users.
These are artificial as they are fixed and defined a priori by the developer,
tailored to the underlying learning algorithm (cf. [30]). When collaborating on a
joint task, common ground on familiar frames helps to negotiate the used frame.
On a smaller time-scale, interaction patterns emerge when contingency is given
over repeated interactions in collaborative activity in unfamiliar communicative
situations that help to establish meaning and can be a measure of successfulinteraction [31]. In the discussion, the question arose whether the interaction
pattern of the interaction between staff and client when going to the bakery wasa frame or rather a script like the frequently mentioned restaurant script [32] as
the typical sequence of events happening when going to the restaurant. Whereas
scripts also involve non-communicative events and events are not constrained tointeraction partners, but interaction partners might change going in and out of
the interaction, PFs are specific to a pair (or group) of interactants and develop
over time. In the restaurant script, roles are just like in the bakery pattern
non-interchangeable.

9

List of Participants• Rachid Alami, LAAS-CNRS, France• Heike Buhl, Paderborn University, Germany• Hendrik Buschmeier, Bielefeld University, Germany• Kary Främling, Umeå University, Sweden• Angela Grimminger, Paderborn University, Germany• Joris Hulstijn, Université du Luxembourg, Luxembourg• Sylvain Kubler, Université du Luxembourg, Luxembourg• Amro Najjar, Université du Luxembourg, Luxembourg• Katharina J. Rohlfing, Paderborn University, Germany• Igor Tchappi, Université du Luxembourg, Luxembourg• Kirsten Thommes, Paderborn University, Germany• Anna-Lisa Vollmer, Bielefeld University, Germany• Henning Wachsmuth, Leibniz University Hannover, Germany• Britta Wrede, Bielefeld University, Germany

10Meeting Schedule
Check-in Day: September 17 (Sunday)• Welcome banquet
Day 1: September 18 (Monday)• Introductory talk by Katharina Rohlfing on what’s new in social aspects
of XAI and current research gaps
• Introduction round with the question of “what is social XAI?”• Invited talk by Angela Grimminger & Hendrik Buschmeier on “MultimodalInteraction Shapes Explanation Dialogues” and discussion• Group activity using Miro Board: Find 5 keywords that characterize AI as
multimodal!
• Group photo shooting• Invited talk by Kary Främling on “Interaction is incremental” and discussion• Group activity using Miro Board: Find 5 keywords that characterize AI as
incremental!
• Invited talk by Anna-Lisa Vollmer on “Interaction is patterned” anddiscussion• Group activity using Miro Board: Find 5 keywords that characterize AI as
patterned!
• Joint look at the 3 dimensions and discussion
Day 2: September 19 (Tuesday)• Discussion on the structuring dimensions• Splitting into 3 groups that provide the dimensions of the social interaction
within XAI: Multimodality, Incrementality, Patternedness• Working in 3 groups: Structure of the handbook and potential entries
within the dimensions• Working in 3 groups: Writing 1 page of an executive summary of thedimension, agreeing on roles and responsibilities for follow-up
Day 3: September 20 (Wednesday)• Discussion on the structure of the book, the publication format, and further
contributors• Working in 3 groups: Reviewing the one page of executive summary andwriting a further version of it considering further contributors and missing
aspects• Excursion and main banquet
11Day 4: September 21 (Thursday)• Discussion on the content of the overarching sections such as Introduction
and Evaluation• Development of a plan for further actions• Wrap up

12

Overview of the Planned Handbook of Social XAI
In the discussions, we agreed on the following target group, structure, and formatof the book.
Target Group for the Handbook The book is for researchers with abackground in Computer Science who are interested in social aspects of AI ingeneral and XAI in specific. It should offer both a quick and a more elaborated
access to the social aspects. Importantly, the readership should find concrete
operationalizations in order to be able to regard the social aspects in theirresearch. For researchers with a background in social sciences, this book should
offer insights into the application of social aspects. It will highlight new facets of
the aspects and new research questions that the application context reveals.
Structure of the Handbook The following structure is planned for the
book. For more details on the chapters, see Section “Summary of New Findings”:
1. Purpose of the handbook
2. Glossary
3. Introduction
4. Multimodality
4.1. Related terminology
4.2. Multimodality in agents
4.3. Nonverbal signals
4.4. Feedback and grounding
4.5. Ambiguity of nonverbal signals
4.6. Synchronization of nonverbal signals in production
4.7. Theoretical aspects of multimodal processing
4.8. Multimodality in explanatory interactions
4.9. Visualization and the use of multimodality to explain
5. Incrementality
5.1. Incremental communication
5.2. Adaptation
5.3. Model of explanandum
5.4. Models of the interaction partner and situation
5.5. History of interaction
5.6. Generation of explanatory content and requirements for social XAI
5.7. Exploration of explaining content
6. Patternedness
6.1. Context for explanations
6.2. Values and norms
136.3. Explainee’s and explainer’s roles and relationships
6.4. Explanation goals
6.5. Responsibilities and their dynamics in explanations
6.6. Engagement in explaining
6.7. Structures underlying explanations
6.8. Practices: How to establish an explaining practice
6.9. Risks in XAI
6.10. Chances of XAI
7. Evaluation
7.1. Measuring the quality of social XAI
7.2. Uncertainly
7.3. Ethical trade offs
In the introduction, the social XAI will be defined as a socially interactive
XAI against the existing background and state of the art. We will argue thatinteraction is the key to involve user. We propose that, rather than being deliveredby the explainer, explanations become tailored when they emerge at the interface
between explainer and explainee, who are both active participants shaping
explanations during a social interaction. Thus, in contrast to explanations thatare implemented in current human-robot interaction (HRI) and human-computerinteraction (HCI), we propose to view explanation as a process that has a direct
influence on explanation generation.
With this argument, the focus on the social interaction within the context
of explanations will be motivated for the book. This context will be identified
as specific and contrasted with other dialogue forms. Two application real-lifescenarios defining the application of XAI will be introduced to provide concrete
examples that highlight the specifics of the context. The three guiding andstructuring dimensions (multimodality, incrementality, and patternedness) will
be introduced.
Finally, the intention of the format (quick access through short and extended
version) will be explained and the target audience will be identified: For persons
with technology background, the entries/chapters should offer clear operationalizations of the social aspects. For researchers with the background in social
sciences, examples of how the context of XAI generates new questions will bemade explicit.
Format of the Handbook The book follows a new format that allows
for both, a quick (2 manuscript pages = 1000 words) and a more elaborated (8manuscript pages = 4000 words) access to the social aspects of the XAI. Each
entry will constitute a social aspect. Overall, the aspects will be structured along
three dimensions (see above).

Summary of Discussions
The three guiding and structuring dimensions of interactive XAI systems – multimodality, incrementality, and patternedness – were discussed in groups critically.
14

Eventually, the participants of the meeting agreed on these dimensions, because
having the function to cluster the Handbook’s entries, they communicate the focus
on interaction clearly. In addition, they capture the properties of a future XAI
system well. More specifically, the property of an XAI system to take advantageof the multimodal signals a human user can send and receive was discussed
as being essential for a successful social interaction as a constructive process.
It should be highlighted that multimodality pertains to a context-dependentutilization of semantic information that can supplement, reinforce, or clarify
what is already presented. Therefore, multimodal signals of communication need
to be regarded when explanations are tailored to and will provide more relevancefor the users.
The context dependency of multimodal signals is strongly linked to how
information is unfolding (and therefore the dimension of incrementality). The
property to interact in an incremental way is necessary for an XAI system tobuild on the contribution of the involved partners and to adapt to each other.
As the interaction unfolds, it is likely that the sequence of actions is specificand follows a format of exchange (related to the dimension of patternedness).
The interaction is patterned in the sense that different contexts and goals willlead to the emergence of different social roles impacting the construction of
explanations.
Even though the three dimensions seem to be intertwined, each of themhighlights specific social aspects.

Summary of New Findings
Multimodality
Angela Grimminger, Hendrik Buschmeier, Joris Hulstijn, Amro Najjar,
Igor Tchappi
Social interaction naturally takes place in face-to-face communication (i.e.,being physically co-present and sharing a referential space) where interaction
partners are able to perceive each other through multiple sensory modalities.
They can hear their partner speak, they can see their partner’s face and bodylanguage. This means that communication uses multiple modalities, for example
speech is accompanied by a manual gesture and prosodic marking. All are closely
coordinated in their timing and work simultaneously. Explanations that people
give each other (e.g., why there is a rainbow) make good use of these modalities(e.g., visualizing the angle at which sunlight falls on raindrops by drawing the
trajectory in the air with a hand gesture). But it is not only the speaker who uses
these multimodal signals. While both partners can see each other, listeners also
use non-verbal modalities, for example to give feedback on their understanding(signaling positive understanding by nodding with their head) or to indicate thatthey would like to ask a question (by looking at the speaker and breathing in).
To make XAI social, this part of the handbook argues that being able to use
and perceive such multimodal signals when explaining AI behavior or decisions
to humans is crucial and (most likely) beneficial, as it can enable explanations
that are tailored to the individual interaction partners’ and situational needs,
state of understanding, emerging goals, etc..

15• In Chapter M1, we will describe the terminology related to that partof the handbook. It introduces sensory-based modalities (vocal/aural,visuospatial), individual modalities to produce signals (face, gaze, hands,
voice . . . ). It is described how they can be used unimodally, but cruciallyalso how they can be combined to form multimodal signals in interaction.
• In Chapter M2, we will describe various artificial agents that might provide
XAI-explanations, it could be disembodied agent (like a voice assistant ora chat bot) or embodied agents such as embodied virtual agents of social
robots (co-located or remote). Embodied agents offer (and it might beargued necessitate) the possibility to communicate multimodally as wellas to make use of space and position. Space and position are useful ascues for identifying roles (link to roles chapter). Embodiment also has
disadvantages. It creates expectations that may not always be met. For
example, noise of the robot movement may hinder speech recognition. Herewe will discuss engineering limitations of the robotics platforms.
• In Chapter M3, we will describe the continuous nature of nonverbal
signals in contrast with the often more discrete nature of verbal means ofcommunication. [production–comprehension]• In Chapter M4, we will write about multimodal feedback and grounding
processes in explanatory interactions.
• In Chapter M5, we will focus on the ambiguity and inherent vagueness ofnonverbal signals, challenges in processing them, but also the (deliberate)use of vagueness and ambiguity to further the communication. It could also
consider incomplete explanation, lack of specificity, contradictory signals.
• In Chapter M6, we will describe how multiple modalities are orchestrated
in production of behavior, specifically focusing on the synchronization
between signals on different modalities and between interaction partners,
the timing of signals as well as how they create and fit into the overall
rhythm of the interaction.
• In Chapter M7, we will discuss theoretical aspects of multimodal processing
including binding between modalities and other theoretical perspectives(Clark’s layers, Levelt’s dual coding theory, enactment, additive, compensatory, and integrated, multimodal-multimedia).
• In Chapter M8, we will inform the reader about how multimodality plays
a crucial role in human explanatory interactions.
• In Chapter M9, we will discuss XAI needs for multi-modality. Often,
XAI resorts to visualization and the use of multi-modality to explain thesophisticated decision of ML algorithms and the autonomous agents.

Incrementality
Kary Främling, Heike M. Buhl, Sylvain Kubler, Kirsten Thommes, Britta
Wrede
Social XAI should operate on an incremental basis, mirroring the inherently
gradual nature of human social interaction. When XAI engages in social exchanges,
16

it must be able to align with the step-by-step, iterative character of humancommunication. This incremental approach involves a dialogic process whereexplanations are shared in smaller portions, enabling a finely detailed and gradualco-construction of explanations between the user and the XAI system.
An incremental explanation strategy requires and allows both parties toadapt to each other and achieve increased understanding. An incremental
explanation strategy also facilitates the identification of misunderstandings bydirectly monitoring each other’s reactions, including verbal and nonverbal cues.
When misunderstandings surface, the explainer can employ social techniques like
repetition, correction or scaffolding, adding or changing the modality, changing
the whole explanation strategy to accommodate the explainee’s needs andexpectations. This interactive process empowers the explainer to enhance theunderstanding of the explainee and update their model accordingly.
To accomplish this nuanced adaptation, the explainer necessitates not only amodel of the subject matter but also an understanding of the explainee’s current
comprehension level regarding the subject. Successful adaptation also hinges
on considering the broader context of the interaction, encompassing factors likesocial roles, physical surroundings, ongoing tasks, application risks (as definedby AI Act), and constraints faced by the explainee.
This contextual awareness extends beyond the immediate interaction andencompasses the history of interactions with the same explainee and otherindividuals within the same social network. As part of a social system, thisdemands consistency in the explanations provided to different explainees, ensuring
not only trustworthiness but also averting potential controversies among social
peers. For instance, explanations should not appear to be contradicting to two
humans who belong to the same group and interact with each other.
A social XAI system should possess capabilities surpassing those of humanhuman interaction. It should deliver pertinent and accurate information, particularly information with legal implications, while also maintaining human agency,
empowering individuals to request explanations and the option to have theirdata forgotten at any time.
Importantly, this process requires the underlying XAI system to also adapt
to the explainee’s needs by providing an interface to query different explanatory
elements. Current XAI approaches mostly consist of one explanatory element
like feature importance or pixel-wise relevance. However, the explanatory needof explainees may exceed this uni-dimensional approach in order to better grasp
the complex causal or relational landscape of the problem domain at hand.
Over time, the explanation process can be automatically improved concerning
the optimal incremental steps in terms of quantity and content. However, thedesign must also systematically vary social explanations and explore whether
new processes of incremental steps may improve the process and avoid pathdependency. This is important, for instance, if the global understanding of
explainees has evolved over time, social changes require new approaches or also
when other modalities of explanations become available.
• Chapter I1: Incremental Communication: will address the problem of
chunking incremental reception and feedback moves.
• Chapter I2: Adaptation will be presented on the basis of monitoring (socialcues); the challenge of finding misalignment and misunderstanding andhow to scaffold understanding will be presented17• Chapter I3: Model of the explanandum: will center around the explanandum(AI’s, explainee’s) and how is it dependent on the domain model and thesituation• Chapter I4: Model of the interaction partner and situation: will provide
introduction into (fixed and dynamic) partner model(s) and how theychange depending on the perspective taking as a cognitive means.
• Chapter I5: History of Interaction: will address both levels, micro andmacrolevel, of repeating or unfolding interaction. It will be of question how
consitency and stability can be established at the macro level. Concepts oftrust, mistrust, trust calibration, and reliability will be discussed against
the evolution of social relations between AI and explainee.
• Chapter I6: Generation of explanatory content and requirements for Social
XAI: will center around the explanandum model and exemplify different
kind of explanations in dependence of vocabulary, abstraction level, andmodality• Chapter I7: Exploration of explaining content: will address the possibilityof dynamically exploring the content under the influence of feedback about
the understanding.

Patternedness
Anna-Lisa Vollmer, Rachid Alami, Katharina J. Rohlfing, Henning Wachsmuth
Social interaction does not evolve randomly. It follows patterns that have been
and are established over time and that are further evolved. The same is true for
explaining process. To lay the ground of research towards social XAI systems, thispart of the handbook is about the ingredients to shape and conduct interaction
in explaining processes. These ingredients are necessary to form patterns thatemerge and help partners to act according to their expectations and roles. Someof the patterns are general to the interaction of two or more people, while others
relate to the specific contexts, goals, and practices of explaining.
The interaction is organized sequentially around a goal. On this goal, thepartners need to agree and maintain it, all along the process of interaction. Thus,
the concept of the goal is central to an interaction being organized in a pattern.
A goal of the human might be to understand the function of the system or to
be informed about uncertainty in the output of the system. The organization
toward a goal happens in a social and physical context and depends on it.
Whereas social context characterizes the explainee’s beliefs, social roles, andexpectations about the dialogue, the physical context relates to relevant aspectsof the actual dialogue setting at hand, including the topic being discussed aswell as the relevant physical environment in which the dialogue happens. Overall,
the process of explanation is structured in joint sequential interaction patterns
that are shaped by a certain background of norms and values, and social orsituational context. Patterns emerge and evolve over time within one explanation
as well as across explanations. On a broader scale, more universal patterns thatare not specific to a set of interaction partners exist (for instance those that areculturally transmitted).
18

Certain patterns are bound to a certain context such that contextual information can be used to determine which patterns to follow. However, the organization
toward a goal is providing clear responsibilities to the participants for its
achievements. Because of the sequential structure of the interaction (actions
being performed sequentially), the responsibilities can be distributed differently
among participants. Thus, partners need to be engaged in the interaction andcommitted to the goal.
It is important to ensure that for the humans, the interactive process willoffer the possibility to contribute to the goal but also latitude to express, allalong the process, their preferences to determine their contributions. In XAI,
responsibilities, contributions and preferences of the explainee are taken into
account continuously. It is the duty of the XAI to assist and facilitate the humancontribution all along the process and to ensure that explainee is committed,
accepts and is willing to continue.
The operationalization of structures, practices, and conventionalizations in
XAI systems entails risks. These include that XAI may even foster the deskillingof people—an effect that can be expected in case that AI systems take over more
and more cognitive tasks as well as in cases in which too much trust exists in the
alleged reliability of the systems’ outputs. It is important to proactively work on
preventive measures for these and other risks, both on the side of the developers
of XAI methods and by the deployers of XAI systems in the real-world scenarios.
Only then, the great potential that XAI systems exhibit can be leveraged. A
successful realization of social XAI will enable people to solve their tasks more
efficiently and effectively by making best use of the inherent capabilities of AI
methods, in professional situations as well as in personal matters. At the sametime, it allows bringing in the humans’ genuine skills and leaving control overthe process to them wherever needed. The understanding of the AI’s decisions
and behaviors gained through social XAI will give people the trust needed toresponsibly and maturely integrate AI into the cognitive processes of everyday
life, but it will also make them aware of the aspects in life that distinguishartificial intelligence from human intelligence.
The part argues that interaction patterns are a beneficial means in explainingthat can be transferred to XAI. When conventionalized global and more finegrained organization of explanations is assumed, explanations can be generated
more easily in such a sequentially organized interaction and at the same time,
multimodal cues and signals of the human interaction partner can be interpreted
more easily.
The planned preliminary section organization is as follows. The text above islargely aligned with this organization:• Chapter P1: Context. This chapter will introduce the notions of social andphysical contexts: social context is formed from cultural context and isapplied in a situation with physical constraints that constitute the physicalcontext. It will be specified how an explanation is embedded into a specificcontext and how these contexts change depending on the scenario andapplication domain.
• Chapter P2: Values and Norms. We will point to the fact that social
contexts are relying on values and norms that are established in the society
or a group.
19• Chapter P3: Roles and Relationships. We will define the notion of social
roles and role relationships (doctor–nurse, doctor–patient) as well asinteraction roles (speaker, addressee, overhearer), all at various levels(utterance, segment, session, relationship). Roles are part of a script orframe, which can be formalized as a dialogue game (see P7). Roles areplayed by specific agents, but only if they qualify. Given a role, it is clear
what the responsibilities and capabilities are (see P5). E.g., the speaker isallowed to indicate who takes the next turn.
• Chapter P4: Goals. In this chapter, we will discuss how the goal of the
explanation process has to be established and agreed upon by the humanand the machine and how it will be maintained and eventually refined or
updated depending on the course of the explanation process.
• Chapter P5: Responsibilities. This chapter builds on the known concept ofstructures and shows that while in an interaction, a structure is followed, it
can be dynamically distributed to the actions of the partners. This requires
a variability in the responsibility for the goal (e.g., a partner might takemore responsibility scaffolding the other partner’s action or performing a
lot of actions necessary to achieve the joint goal)• Chapter P6: Engagement. We will not only point out that an agent (ahuman or a system) needs to engage in an interaction in order to commit
to the goal, this chapter will further link to the concept of human agency(see below under “notes”) and how the human should stay the master ofthe game.
• Chapter P7: Structures. This chapter will present recurring structuresof interactions (and their goals) as they occur in explaining processes,
from the single dialogue acts and signals in individual turns and micropatterns, to the pragmatic frames that constitute the composition of series
of interaction mechanisms, to patterns of common types of dialogue asa whole. It will establish abstract patterns visible across all or certain
contexts and will detail how they are instantiated for specific social andsituational contexts given.
• Chapter P8: Practices and Conventionalizations. In this chapter, we
will talk about the formation of interaction patterns and the role ofrepetition in this process. We will provide an account on how practices
and conventionalization pertain to explaining and to identify important
prerequisites or ‘biases’ for social XAI systems.
• Chapter P9: Risks. This chapter will highlight main risks arising forindividuals and societies through the increasing prevalence of AI systems,
focusing on those that are particularly relevant or even amplified in thecontext of XAI. It will shed light on what to particularly pay attention to
in order to alleviate these risks as far as possible.
• Chapter P10: Chances. Finally, this chapter will elaborate on the chancesthat emerge from a systematic design and realization social XAI for theacceptance and effective use of AI in professional applications and society.

20Evaluation
Kirsten Thommes, Joris Hulstijn, Henning Wachsmuth, Suzana Alpsancar
Whether or not social XAI is successful in terms of generating understandable
explanation tailored to the user needs to be evaluated. In the best possible
scenario, the explanation process would be self-optimizing in the long-run,consecutively improving the explanation procedure. Previous research measuring
the interaction quality of systems frequently relies on correlations rather thanon causal identification strategies. This is the case, for instance, if explainees arequestioned about the perceived interaction quality, understanding or trust in
the system post-hoc. Next to many commonly known effects of questionnaire
studies, e.g. evaluator-demand effects, this approach also bears the problem
of systematically missing causality and instead measures correlations only,
resulting in a missed opportunity to improve the system. This chapter discusses
processes of testing for causality instead of correlations. Moreover, we discuss theoperationalization of potential outcomes of Social XAI, particularly explainees’
attitudes such as trust, likeability of interaction, and aversion and behavioral
responses such as understanding, reproducability of explanations, or decisionquality. Next to measures of effectiveness, the designer of Social XAI systems
need to assess how they navigate ethical trade offs. For instance, one may ask
whether Social XAI should mimic human social cues and lead explainees toanthropomorphize the system. On the one hand, human-like explanations may
be more understandable than other types of explanations. On the other hand,anthropomorphize the system may eventually even result in false expectationsuch as empathy or emotions. We discuss the most common ethical trade offs
and how to assess them.
• Chapter E1: Measuring the quality of social XAI. In this chapter, adequate
measurements assessing the quality of social XAI will be identified anddiscussed. We will focus on tests for causality instead of correlations andtypical evaluation criteria and their operationalization.
• Chapter E2: Uncertainty. In this chapter, uncertainty as a function of(X)AI is introduced and discussed.
• Chapter E3: Ethical trade-offs. We will tackle important ethical aspects
such as accountability, transparency, inclusiveness of XAI systems. We willfurther explain some of the most common ethical trade offs in Social XAI
and how to assess them.

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