On the Importance of User Role-Tailored Explanations in Industry 5.0

Inti Gabriel Mendoza

1 a

, Vedran Sabol

1,2 b

and Johannes Georg Hoffer

3 c

Know-Center GmbH, Sandgasse 36, Graz, Austria

Graz University of Technology - Institute of Interactive Systems and Data Science, Sandgasse 36, Graz, Austria

voestalpine B

OHLER Aerospace GmbH & Co KG, Mariazellerstraße 25, Kapfenberg, Austria

Keywords:

eXplainable AI, human-AI Interface Design, Explanations, Personalization, Process Engineering.

Abstract:

Advanced Machine Learning models now see usage in sensitive ﬁelds where incorrect predictions have serious

consequences. Unfortunately, as models increase in accuracy and complexity, humans cannot verify or validate

their predictions. This ineffability foments distrust and reduces model usage. eXplainable AI (XAI) provides

insights into AI models’ predictions. Nevertheless, scholar opinion on XAI range from ”absolutely necessary”

to ”useless, use white box models instead”. In modern Industry 5.0 environments, AI sees usage in production

process engineering and optimisation. However, XAI currently targets the needs of AI experts, not the needs of

domain experts or process operators. Our Position is: XAI tailored to user roles and following social science’s

guidelines on explanations is crucial in AI-supported production scenarios and for employee acceptance and

trust. Our industry partners allow us to analyse user requirements for three identiﬁed user archetypes - the

Machine Operator, Field Expert, and AI Expert - and experiment with actual use cases. We designed an

(X)AI-based visual UI through multiple review cycles with industry partners to test our Position. Looking

ahead, we can test and evaluate the impact of personalised XAI in Industry 5.0 scenarios, quantify its beneﬁts,

and identify research opportunities.

1 INTRODUCTION

Machine learning (ML) and artiﬁcial intelligence (AI)

models continue to evolve and improve. AI has al-

ready achieved human superiority, as it may outper-

form humans in tasks like pattern recognition (He

et al., 2016). Thus, AI models see usage in many

areas, from video games, over criminal recidivism

(Kennedy et al., 2022), to production engineering and

optimization (Hoffer et al., 2022), and more. Some of

these areas are of paramount importance to get right.

For example,

failing to predict the need for maintenance of a

machine can interrupt a production process for days,

leading to huge losses. Nevertheless, usage of ML

models in critical areas will continue, and it should

- their efﬁcacy in most cases is too valuable. How-

ever, as the Russian proverb goes, “trust but verify”.

The need for explainable artiﬁcial intelligence (XAI)

is thus also increasing.

XAI is a subset of AI which aims to explain a

https://orcid.org/0000-0001-6779-7948

https://orcid.org/0000-0002-0599-445X

https://orcid.org/0000-0002-2441-7279

model’s predictions. In essence, a model is explain-

able (or interpretable) if the user can understand why

a model produced a speciﬁc output prediction. On

the other hand, accurate but very extensive expla-

nations do not mean helpful or useful explanations.

Some scholar dispute the need for XAI suggesting use

of white-box models, which often includes an accu-

racy loss. In any case, through useful explanations,

a model made transparent or explainable, should be

trusted by users. (Miller, 2019).

Few XAI research integrates social and be-

havioural sciences ﬁndings. This lack of transdisci-

plinarity is worrisome as researchers keep creating

explanation quality metrics. Social and behavioural

sciences have already deﬁned what makes up a proper

explanation (Miller et al., 2017). Furthermore, XAI

methods seldom target the user. The assumption

stands that if researchers understand the explanations,

the target user will. Unfortunately, this is an exam-

ple of “logic gymnastics” that does not follow. XAI

methods are supposed to provide explanations. Ex-

planations, as a whole, are a form of conversation.

In conversations, humans tailor conversations to the

recipient’s level. Thus, XAI must also do this when

Mendoza, I., Sabol, V. and Hoffer, J.

On the Importance of User Role-Tailored Explanations in Industry 5.0.

DOI: 10.5220/0011748300003417

In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 2: HUCAPP, pages

243-250

ISBN: 978-989-758-634-7; ISSN: 2184-4321

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

243

presenting explanations to end users.

We argue that XAI models’ output explanations

are not the ultimate output. These explanations must

take the work environment and the users’ abilities to

understand the explanations into account when pro-

viding the ultimate output. Thus, we, the authors, be-

lieve that in the current XAI research landscape, there

is a need for further handling of explanations and tai-

loring the communication to speciﬁc target groups.

This position paper provides the steps we intend

to follow to develop a proof-of-concept demo, with

focus on tailoring XAI to user archetypes in produc-

tion processes. Based on that, we intend to show that

automatic output manipulations are possible, helpful,

and, most importantly, needed.

The structure of this position paper is as follows:

Section 2 gives related works on XAI and social sci-

ence’s research on explanations to support our Posi-

tion that further handling of XAI output is needed.

Section 3 describes our setting, clariﬁes our Position’s

context, and validates the need for user-targeted ex-

planations. Section 4 discusses the validity of our Po-

sition in a more general case. Section 5 describes how

we intend to prove our Position empirically.

2 RELATED WORK

This section provides an overview of the current XAI

landscape and information obtained from a literature

survey, including papers with research from the social

sciences. The main focus of our literature survey was

on explanations. However, this section will also show

the need to treat XAI research interdisciplinarily.

2.1 Explanations According to XAI

People should be able to challenge AI models’ pre-

dictions for critical tasks. The reasoning behind the

prediction should not be “because the model said so”.

This case is one of many factors contributing to the

increased need for Explainable AI (XAI) research -

even being explicitly pushed by the European Com-

mission (Carvalho et al., 2019).

XAI is currently considered another ﬁeld in com-

puter science where its models produce human-

understandable explanations for interpreting AI mod-

els. However, due to the ﬁeld’s relative youth, the

most basic XAI concepts must be clariﬁed. For ex-

ample, researchers use the terms “explainability” and

“interpretability” interchangeably. Furthermore, there

has yet to be an agreed-upon method to assess an XAI

system’s explanation’s quality (

Simi

c et al., 2022).

Most notably, researchers have yet to reach a con-

sensus on the nuclear goal of XAI. Most say that the

nuclear goal is to establish trust in users (Sperrle et al.,

2020). Besides model validation, another commonly

stated goal is model debugging, i.e., identifying rea-

sons for wrong predictions. Finally, our literature sur-

vey shows a reluctance to admit that XAI is not a pure

computer-science discipline. The ﬁeld of XAI shares

the academic landscape with the social sciences. Re-

gardless of these shortcomings, XAI methods have al-

ready seen the light of day in areas like medical diag-

noses, autonomous vehicles, and process engineering,

among many more.

2.1.1 XAI Used in Process Engineering

XAI already sees usage in process engineering. For

example, the car manufacturer, Volvo, has already im-

plemented XAI to optimize its process lines. Here we

will describe the utilization of XAI in process engi-

neering in more detail.

(Mehdiyev and Fettke, 2021) described Volvo’s

problem context and ultimate solution. Volvo main-

tains three repair lines in charge of repairing broken

parts in their other process lines, one more special-

ized than the previous. However, lesser specialized

lines kept misjudging task complexities and forward-

ing them to deeper lines, creating a bottleneck prob-

lem. Researchers, to combat this, developed a (black-

box) AI model to predict which repair line should

handle a given task. However, this model must work

with humans; thus, its inability to explain its decisions

fomented distrust and lack of usage. To circumvent

this, they integrated an XAI layer and, in so doing,

developed a framework to develop XAI methods to

tackle this type of problem.

2.2 Explanations According to Social

Sciences

(Puiutta and Veith, 2020) notes the need for be-

havioural sciences references in XAI papers. Expla-

nations are an area already studied in the social sci-

ences. AI researchers should refrain from attempting

to reinvent the wheel when deﬁning proper explana-

tions. Furthermore, discoveries and knowledge of so-

cial sciences on explanations are abundant, available,

and valuable. It is then unreasonable not to take ad-

vantage of this knowledge. Our literature survey iden-

tiﬁed a set of helpful social sciences knowledge that

is paramount and useful for XAI research and usage.

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244

2.2.1 Explanations Are Contrastive

Behavioural science teaches us that humans prefer

contrastive explanations. When humans ask “why”

questions, they implicitly ask for a contrastive case

(Mannheim et al., 1990). Humans want to know why

a unique explanation is correct instead of a different

one. When humans turn to XAI for explanations, they

ask these “why” questions, thus posing a unique chal-

lenge for XAI research. Proper implementation of

this knowledge will ultimately lighten the load of pro-

viding a dense, complete, and thus improper explana-

tion (more on this statement later). Furthermore, an

explanation does not have to cover or provide all cases

- it does not have to ﬁnd all cases (Lipton, 1990).

2.2.2 Attribution Theory & Temporal Causality

Social sciences developed attribution theory. This

theory tries to explain how people attribute causes

to consequences. Generally, there are two types of

attributions: social attribution and causal attribu-

tion. Social attribution describes how people attribute

others’ behaviours. People attribute causality based

on the acting party’s characteristics. Successful ac-

tions are different from unsuccessful actions. Unsuc-

cessful actions often have an unmet precondition at-

tributed to them. This theory is essential when design-

ing XAI methods, particularly when an explanation

comes from an intentional action viewed as a cause.

Causal connection describes how people connect

causes. They do so by seeing a prediction based on

how it would have changed if it had stemmed from a

different action (Kahneman and Tversky, 1981). If the

“changed” action is too far from the past, the causal

chain might be too complex to imagine or see its rel-

evance. This theory focuses on how people choose

which action to “undo”. Actions that are proximal,

abnormal, and more “controllable” are often chosen

to create this causal chain (Miller et al., 2017).

2.2.3 Explanations Are Tailored to the

Recipient’s Knowledge

As ﬁeld experts, it is not uncommon to think of spe-

ciﬁc ﬁeld-speciﬁc knowledge as fundamental or com-

mon knowledge. However, even something as basic

as terminology can elude “regular” (non-ﬁeld experts)

people. Moreover, it is not uncommon to think that

learning this knowledge is a trivial task - it is not so.

It is thus troublesome when XAI researchers, who de-

velop explanations-producing XAI models, can un-

derstand these explanations and incorrectly assume

that end-users will too. These researchers, there-

fore, assume that their explanations are good expla-

nations. This ﬂawed thinking is why (Miller et al.,

2017) warns of “inmates running the asylum”.

2.3 Supporting Our Position

The abovementioned Volvo example proves the ap-

plicability of XAI methods in process engineering

contexts. Furthermore, it illuminated the problem of

workers not trusting - and thus not using - AI mod-

els to help them and how an XAI layer implementa-

tion alleviated some of these problems. This insight

supports our Position that XAI methods are helpful

in process engineering contexts to increase workers’

trust and usage of AI models. Furthermore, our lit-

erature survey on explanations supports our Position

that following social sciences guidelines creates help-

ful explanations that foment user trust, and XAI me-

thods should follow them (Miller et al., 2017).

3 USER-TAILORED XAI

This section elaborates on what “further handling of

XAI output” means. First, we describe an example

problem context where a black-box ML model per-

forms valuable predictions. Moreover, we create an

XAI model that explains the initial model’s predic-

tions. Finally, once we obtain candidate explanations,

we handle and manipulate them in order for the re-

sulting explanations to conform with social science’s

research on what constitutes a proper explanation.

3.1 Problem Context

For our problem context, we set ourselves in a pro-

cess engineering setting. Speciﬁcally, we want to help

a company manufacture complex parts efﬁciently.

Input materials (materials as obtained by resource

providers) must undergo work in multiple process

chains - each consisting of process steps - to reach an

acceptable output state. Inaccuracies, mistakes, and

blunders are bound to occur and create different con-

sequences. For example, an item with an inaccuracy

is repairable, but an item with a mistake must be dis-

carded and sold off at a loss. An item with a blun-

der might not even be worth selling. It can also hap-

pen that an item repair is attempted (incurring repair-

ing costs) but ultimately reassessed as un-salvageable.

The opportunity cost of working on an ideal or sal-

vageable item instead is, of course, incurred as well.

Thus, it would be beneﬁcial to create an AI model

that can predict with sufﬁcient accuracy whether some

material, after processing, will be transformed into an

acceptable output material. Since we are working in

On the Importance of User Role-Tailored Explanations in Industry 5.0

245

the process engineering area, the AI and the employ-

ees must work together. Teamwork involves user trust

in the model, and, as Volvo’s experiences show, the

AI must explain its predictions to work with humans

productively. This model will have to work with each

identiﬁed user archetype and thus be able to help in

material processing, process design, and data mining.

3.2 Identifying User Archetypes

Requirements

Explanations must tailor to the recipient’s knowledge

and working environment; thus, we must identify

our user demographic and their (explanation) require-

ments. Through numerous talks with our business

partners, we identiﬁed three relevant user archetypes

- the Machine Operator, Field Expert, and AI Expert -

and their UI and explanation requirements. The Ma-

chine Operator only needs to identify obvious blun-

ders in the AI model related to one process step, but

has a little time to do so. The Field Expert is knowl-

edgeable and needs to verify the AI model’s knowl-

edge and identify blunders, mistakes, and inaccura-

cies in the model predictions. Finally, the AI ex-

pert must identify reasons for wrong model predic-

tions, in order to ﬁx it. These three user archetypes

are common in most AI-augmented process engineer-

ing/optimization use cases, even ones different from

those in this scope. Talks with our business partners

support this fact. In general, the AI expert creates

the model, the ﬁeld expert veriﬁes the model’s out-

put, and an operator will only want to use the model

that can be trusted. Thus, our planned framework is

helpful in the general scope of process engineering.

3.3 Decision Trees

Decision Trees (DTs) are rule-based white-box mod-

els. Once induced, it is possible for humans to fol-

low a prediction on reasonably sized trees. DTs group

data by creating discrimination criteria (nodes) to ex-

tract and label the structure of the data (Dasgupta

et al., 2020). These criteria are attribute thresholds.

If an input attribute falls within a speciﬁc range, the

prediction “goes” in a particular direction down the

tree. The prediction branch consists of the traversed

nodes and the ﬁnal leaf. This leaf is the prediction.

We intend to use DTs as model-agnostic surrogate

models (metamodels) of an accuracy-focused (black-

box) AI model. The AI model becomes an oracle to

create training data for the DT-inducing process. This

way, we extract what the AI model learned, including

the structures leading to erroneous predictions, which

are helpful for model debugging (Vilone et al., 2020).

Shorter trees use fewer attributes, thus only em-

ploying essential ones and sacriﬁcing accuracy. On

the other hand, the longer the tree is, the more accu-

rate and speciﬁc it will be, as branches can accom-

modate more discrimination points (nodes). Since

DTs can be parametrizable, we propose that they can

be used to imitate proper explanations. Selection

of the tree depth for the DT-inducing process offers

the choice between generating accurate but complex

(deep DT) or approximate but simple (shallow DT)

explanations. Further, selection of the truly informa-

tive nodes provides an opportunity for personaliza-

tion, as overwhelming a user with obvious or irrele-

vant facts would be counterproductive.

We note that explanations are contrastive. Any

branch different from DTs’s prediction branch can be

considered a contrastive branch. Thus, these are the

“what if” branches we can use to provide contrastive

elements in explanations. Next, branches can mimic

attribution theory because the nodes in the prediction

branches are akin to chain links: prediction branches’

nodes depend on each other - a node depends on the

previous node. Furthermore, the location of a node in

the prediction branch mimics a temporal value. Nodes

at the start of the prediction branch are “farther” pre-

conditions than nodes closer to the prediction leaf.

3.3.1 Main UI

In our problem context, all three user archetypes use

the same base UI. As seen in Figure 1, we identiﬁed

three main areas: left, top, and bottom. The left area

allows the user to select the process step they want

to work in and the materials they want to process.

The user can then select and group the available ma-

terials to process them - which have been automati-

cally colour-coded by an (out-of-scope) clustering al-

gorithm to identify similar materials helpfully.

The top area has two elements: a Parallel Coor-

dinates visualization and an area containing the De-

sign Document for the current process step. Parallel

Coordinates shows batch materials’ control measure-

ments to help with material selection and grouping.

The Design Document allows the user to obtain the

information they need about the current process step.

The bottom area has three elements: the Mate-

rial Measurements table, the Parameter Settings Vec-

tor (PSV) table (including “Go” buttons), and the AI-

Predicted Output measurement table. The ﬁrst col-

umn of the PSV-table displays the default parameter

settings used in the current process step. The second

column contains an AI-suggested PSV that considers

the selected materials with their exact measurements.

The user can opt to receive an explanation for this

prediction. The user can also use their custom PSV

HUCAPP 2023 - 7th International Conference on Human Computer Interaction Theory and Applications

246

Figure 1: Main (base) UI.

within some allowed threshold (third column). The

rightmost table allows the user to see the target output

measurements, as detailed by the design document,

and the AI-predicted target outcomes measurements.

In this table, an AI model predicts the (average) target

output if the selected materials underwent the process

using each parameter vector. The user can obtain ex-

planations for each prediction at the bottom of the ta-

ble. Clicking to obtain explanations opens the Expla-

nation UI view, which tailors the explanations to each

user archetype’s needs and requirements. Finally, in

the “Go” buttons, the user can select which PSV to

process the selected materials.

3.3.2 Explanation Differences

According to social science research, each user

archetype needs a different explanation. Figure 2

shows how we intend to display explanations. The

top part will always be visible and contain informa-

tion on the selected materials, PSVs, and AI-predicted

output target measurements. Users can change expla-

nations to other output targets or use different PSVs.

The bottom part is scrollable and contains tailored ex-

planations as contributions of each control measure-

ment and process parameter to the predicted output

measurements in a visual format (detailed design to

be decided upon). Tailored explanations are essen-

tial, and each user archetype has (already described)

unique requirements. Therefore, explanations for Ma-

chine Operators must be as concise as possible, as

their time is minimal. Field Experts have more time,

so explanations can be more precise and tailored to

their domain knowledge, enabling them to validate

the model’s knowledge. Explaining irrelevant or ob-

vious facts is not only useless but also counterproduc-

tive. Finally, AI experts should be free to choose see-

ing as many different explanations as needed to un-

derstand the model’s behaviour.

3.4 Supporting Our Position

Large DTs can provide prediction branches, which

are the source of explanations, that are big enough to

overwhelm users. Therefore, DT models (and white-

box models that do not consider user overwhelming)

are not XAI models. It takes more than transparency

to make an explanation understandable and useful.

We have described how we propose DTs can produce

explanations that follow social science’s guidelines

for good explanations. The ability to parametrize DTs

and their mimicry of social sciences theory of expla-

nations mean that explanation tailoring is possible.

We could tailor them to each of our user archetype’s

knowledge and serve their unique requirements.

4 EVALUATION CYCLES

UI and XAI explanations design occurred with indus-

try partners to aid one of their process engineering di-

visions. This collaboration ensured that the UI design

decisions were always consistent with their process

On the Importance of User Role-Tailored Explanations in Industry 5.0

247

Figure 2: Mock-up design of Explanations UI.

engineering and operation needs. The constant evalu-

ations, revisions, and feedback resulted in the ultimate

state of the UI and user archetype requirements. We

elaborate on this process in this section.

4.1 Continous Evaluation

A continuous evaluation has continued for almost one

year, with evaluation iterations occurring every two

weeks for six months and fewer periodic evaluations

afterwards. The initial three months consisted of

mockup designs of the base UI (Figure 1, and the

next three consisted in implementing these mockups

into a clickable UI. The last six months have con-

sisted of minor UI improvements and the integration

of synthetic data and the initial AI and XAI meth-

ods. In the evaluation sessions, we presented the cur-

rent progress and industry partners would then evalu-

ate the included functionalities and ensure that the in-

cluded functionality was valuable and necessary. This

functionality assurance occurred in consideration of

each user archetype. In addition, another partner con-

ducted grassroots interviews with real users to get

their perspectives concerning their acceptability of a

tool to help them during their tasks. These interviews

were included in our iterative evaluations, further con-

tributing to our UI design decisions.

4.1.1 Following Guidelines for (X)AI UIs

Presenting differently tailored explanations for one

prediction or algorithm produces different effects on

users. Therefore, differently tailored explanations

will have their advantages and disadvantages (Cai

et al., 2019). Also, the medium and techniques em-

ployed to present these explanations are important. To

address this, Microsoft (Amershi et al., 2019), Google

(Google, 2022), and Apple (Apple, 2022) published

guidelines on presenting explanations to users. In ad-

dition, Microsoft emphasizes meeting user expecta-

tions, Apple emphasizes smooth user experience, and

Google emphasizes concepts that the developer must

consider rather than the user.

While designing our (X)AI-assisted process engi-

neering tool, we followed the guidelines published by

these three companies. Following Microsoft’s initial

guidelines, we present to the user exactly what they

can do with the tool. For our use case, the user must

be able to do two things: see the materials about to be

processed and process them, either using default, AI-

predicted or self-deﬁned parameters. Our UI allows

and helps them to do just that and only that. When AI

predictions appear implausible, or more information

is needed, the UI allows easy access to explanations

or the design document. Over time we aim to provide

future functionality that would allow user ﬂagging of

suspicious AI results and allow the tool to adapt as

user’s knowledge increases.

Following Apple guidelines, we have identiﬁed

the role of AI within our (X)AI tool concept. We

make it clear, through colour highlighting, which UI

areas are relying on the AI results. We initially hide

the explanations for simplicity, but make them acces-

sible on-demand through a single button. As Apple

suggests, obtaining the explanations is a voluntary ac-

tion.

Following Google guidelines, we aim to present

trustworthy explanations. We will achieve this by fol-

lowing social sciences guidelines of what constitutes

a proper explanation and providing explanations that

follow these guidelines. Ideally, AI-suggested param-

eters should be more successful and desirable than the

other non-AI-enhanced parameter vectors, so it makes

sense for users to accept them. Furthermore, to use

this model, our tool will nudge users to use these sug-

gestions more often by explaining the predicted out-

put when using these suggestions. Finally, in order

not to lure users into false security, we will need reli-

able quality metrics to estimate and conﬁrm the faith-

fulness of the explanations.

4.2 Supporting Our Position

After multiple evaluation cycles, our (X)AI UI con-

cept is ready for further steps.Initial internal reviews

of the UI’s usability hint at positive user experiences.

Satisﬁed industry partners also vouch for the value

our UI will provide to their process engineering divi-

sion. Finally, our attempts to prevent hyperspecializa-

tion in our industry partner’s process engineering di-

vision allow us to present our UI as a possible general

solution. This solution includes the generalization of

user archetype explanation needs, further supporting

the value of our Position could have.

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248

5 EXPERIMENT PROPOSAL

Our literature survey outlines three challenges of XAI

within the scope of this paper. These are:

1. Involve end users when developing (X)AI meth-

ods.

2. Marry XAI research with social sciences and hu-

man behaviour studies.

3. Standardize studies that consider user traits like

personality and education.

To tackle these challenges, we aim to test the follow-

ing hypothesis: we propose that the current XAI mod-

els’ outputs must undergo further transformations to

be proper explanations. Performing no transformation

should result in (generally) users not understanding a

prediction as much or as quickly as possible.

5.1 Experiment Proposal

To test our hypothesis, we will set up an A/B ex-

periment for each user archetype using real user

archetype-belonging subjects. Once we allocate a rea-

sonable number of test subjects, we will divide them

into two groups. One will receive the explanation

in its untailored form, and the other will receive the

transformed, tailored version. Next, the subjects will

attempt to comprehend their explanation in a constant

allotted timeframe. This sequence will occur several

times on the same data set for multiple explanations.

Afterwards, we will assess the knowledge they re-

tained about the process and how they rate the expla-

nations received. Finally, we will compare the scores

of each group against each other, as well as the time

and the effort it took for subjects to understand the ex-

planations. Compiling all results will determine if the

experiment supports or undermines our hypothesis.

5.2 Expected Outcome

Given proper handling of XAI outputs, a shorter, tai-

lored explanation should be more comprehensible and

thus easier to remember and learn. Moreover, present-

ing something easily digestible should also yield more

satisﬁed users. In conclusion, we expect tailored ex-

planations to be rated as more understandable, more

useful and more trustworthy. We expect our hypothe-

sis to be signiﬁcantly supported, providing an empiri-

cal support for our Position.

ACKNOWLEDGEMENTS

This work was supported by the BrAIN project.

BrAIN – Brownﬁeld Artiﬁcial Intelligence Network

for Forging of High Quality Aerospace Components

(FFG Grant No. 881039) is funded in the framework

of the program ‘TAKE OFF’, which is a research and

technology program of the Austrian Federal Ministry

of Transport, Innovation and Technology.

Know-Center is funded within the Austrian

COMET Program, under the auspices of the Aus-

trian Federal Ministry of Climate Action, Environ-

ment, Energy, Mobility, Innovation and Technology

(BMK), the Austrian Federal Ministry of Labour

and Economy (BMAW) and by the State of Styria.

COMET is managed by the Austrian Research Pro-

motion Agency FFG.

We also thank colleagues from the voestalpine

OHLER Aerospace GmbH & Co KG and Virtual

Vehicle Research GmbH for their input and fruitful

discussions concerning user requirements and user

acceptance.

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