11institutetext: Lab for Human-Centered AI, Augsburg University, Augsburg
86159, Germany 11email: {alexander.heimerl, silvan.mertes, tobias.baur,
<EMAIL_ADDRESS>22institutetext: German Research Center for
Artificial Intelligence (DFKI), Saarland Informatics Campus D3.2, Saarbrücken,
Germany 22email: {schneeberger<EMAIL_ADDRESS>33institutetext: Department
of Psychology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen
91054, Germany 33email: {linda.becker<EMAIL_ADDRESS>
# “GAN I hire you?” - A System for Personalized Virtual Job Interview Training

Alexander Heimerl 11 Silvan Mertes 11 Tanja Schneeberger 22 Tobias Baur 11
Ailin Liu 11 Linda Becker 33 Nicolas Rohleder 33 Patrick Gebhard 22 Elisabeth
André 11

###### Abstract

Job interviews are usually high-stakes social situations where professional
and behavioral skills are required for a satisfactory outcome. Professional
job interview trainers give educative feedback about the shown behavior
according to common standards. This feedback can be helpful concerning the
improvement of behavioral skills needed for job interviews. A technological
approach for generating such feedback might be a playful and low-key starting
point for job interview training. Therefore, we extended an interactive
virtual job interview training system with a Generative Adversarial Network
(GAN)-based approach that first detects behavioral weaknesses and subsequently
generates personalized feedback. To evaluate the usefulness of the generated
feedback, we conducted a mixed-methods pilot study using mock-ups from the job
interview training system. The overall study results indicate that the GAN-
based generated behavioral feedback is helpful. Moreover, participants
assessed that the feedback would improve their job interview performance.

###### Keywords:

Job Interview Training Generative Adversarial Networks Counterfactual
Explanations Engagement

##  1 Introduction

Given the global economic situation, we are confronted with a (post-) Covid-19
pandemic world, one significant issue many countries around the globe face is
the rising number of people Not in Employment, Education, or Training (NEETs).
NEETs often suffer from underdeveloped socio-emotional and interaction skills
[14, 9], such as a lack of self-confidence and perception of one’s strengths.
This circumstance might affect their performance in various essential
situations, such as job interviews. They need to convince a recruiter of their
fit in a company by actively engaging in interviews. Interviewers consciously
or unconsciously heavily rely on social cues to assess the fitting. The amount
of positive engagement a candidate shows towards the interviewer may play a
central role in whether the candidate is suitable. Delroy et al.[21] found
that active integration behaviors such as engagement, laughing, and humor led
to better performance ratings and, therefore, to a higher rating of overall
recruitability. As many people lack such social skills, many countries have
specialized inclusion centers before the pandemic, meant to aid people to
secure employment through coaching by professional practitioners. However,
this approach is costly and time-consuming, and given lockdown situations
often cannot be performed physically. In this context, technology-based
solutions might be reasonable alternatives to the existing human-to-human
coaching practices.

This paper presents a feedback extension to an existing job interview training
environment that uses a socially interactive agent as a recruiter and an
engagement recognition component to enable the virtual agent to react and
adapt to the user’s behavior, and emotions [3]. There, during a preparation
phase, trainees were instructed to show certain behaviors in specific job
interview training situations and got feedback from the virtual agent on
whether they could perform these instructions correctly. This training aims to
help improve social skills that are pertinent to job interviews. The new
feedback extension employs an eXplainable AI (XAI) method based on
counterfactual reasoning for generating verbal feedback about observed social
behavior. This approach allows communicating features (e.g., no eye contact,
closed body posture) that weaken the overall job interview performance. There,
we make use of _counterfactual explanations_ , explaining to a user that a
modified version of her/his social behavior would have led to a better
behavior rating. In the context of XAI, counterfactual explanations have
proven their capabilities to lead to a reasonable explanation satisfaction,
since the user is not only presented with information about _which_ features
are relevant but further, even if implicitly, with information about _why_
those features are relevant.

Figure 1: Job interview training system with GAN-generated recommendations.

The introduced feedback extension is based on a deep learning classifier
predicting the user engagement in job interview situations that uses
multimodal feature (e.g., gaze, body posture, or gestures) representations of
the trainee as input. We exploit the concept of counterfactual explanations to
show what the user would need to change to appear more engaged. Therefore, a
GAN-driven counterfactual explanation model is trained that transforms those
feature representations to corresponding counterfactual explanations, i.e.,
the feature representations are changed so that the user would have appeared
engaged. The explanation generation compares the counterfactual feature
vectors with the original feature vectors to derive textual recommendations
automatically. Finally, they are presented to the trainee by a socially
interactive agent in the role of a job interview coach. Figure 1 shows a
schematic overview of our approach.

##  2 Related work

Due to the complexity and importance of job interviews, automatic training
approaches have been developed to improve the performance of the candidates.
Multiple simulated training systems have been proposed over the years that
combine social signal interpretation and virtual agents [2, 10, 25]. Another
example by Gebhard et al. [8] introduced a serious game simulation platform to
train social skills. They showed that their training systems can be utilized
to teach individuals how to display adequate socio-emotive reactions during
job interviews. Naim et al. [19] introduced a framework for the automatic
assessment and analysis of job interview performance. Their proposed system is
capable of reliably predicting ratings for interview features such as
friendliness, excitement and engagement. Through analysis of the learned
feature weights of their regression model they were able to derive general
recommendations on how to behave during job interviews, e.g. use filler words
less frequently. However, those recommendations are not specific to a
situation but rather general guidelines. Takeuchi et al. [25] developed a job
interview training system that provides automatically generated, situation-
specific feedback by analysing nonverbal behavior and comparing it to a
reference model of ideal nonverbal behavior. The feedback generation was
accomplished by defining weights for the shown improper nonverbal behavior in
accordance with its importance during the interview.

Even though providing feedback or guidelines based on weight prioritization
may produce satisfactory results, those approaches fail to take the interplay
of different shown nonverbal behaviors into account, since each behavior is
considered on its own. Imagine a job candidate that is appearing to be low
engaged due to a closed body posture with crossed arms and additionally isn’t
giving his interlocutor much nonverbal feedback like nodding. For such a case
it is not enough to consider each behavior or corresponding feature on its
own. If we choose to recommend giving more nonverbal feedback, we need to be
also aware of how the person is being perceived while changing one of his
behaviors. In our case, this would result in a person nodding while still
maintaining a closed body posture with crossed arms. Therefore, we argue that
it is important to consider the interplay of features when generating
personalized feedback and nonverbal behavior recommendations. By utilizing a
counterfactual reasoning process we are able to generate feedback that models
a holistic recommendation for nonverbal behavior adjustments. This reasoning
process tries to answer the question of how should the person have behaved to
be perceived as more engaged. For this purpose, the underlying GAN tries to
change simultaneously as many features as needed while at the same time trying
to change as few features as possible and therefore guaranteeing meaningful
recommendations.

##  3 Recommendation Generation

The next sections offer an overview of the different components we implemented
to generate behavioral recommendations that point out how the user should have
behaved to appear more engaged.

###  3.1 Feature Extraction

In order to train a model for engagement recognition and recommendation
generation, we modeled a high-level engagement feature set that can be easily
interpreted. The feature set consists of 18 metrics mapping facial behavior,
body language and conversation dynamics.

During conversations, the face usually occupies most of the interlocutors’
attention. A lot of important information regarding the level of engagement
can be extracted from the face, respectively the head. In fact, there are
multiple studies that found a correlation between head movement / gaze
behavior and conversational engagement [11] [4] [20]. Inspired by those
findings we defined several features that represent the overall movement of
the head and gaze behavior. Moreover, we considered the valence of the face
calculated from the facial action units that have been extracted with OpenFace
[1].

Another modality we take into account is the general body language of the job
candidate. The alignment of the body and the limbs play an important role in
broadcasting the state of engagement [18]. Interlocutors, that are engaged
during a conversation, align their bodies to each other, as described in [12],
“to create a frame of engagement”. We tried to cover the general behaviour of
the body, as well as specific gestures or poses that are connected to
engagement. We defined a group of features that are mainly inspired by the
coding system introduced in [6]. It contains several metrics to map the
orientation and movement of the joints. Those metrics represent amongst other
things the overall level of body openness. Besides that, we also calculate a
cumulative value over all joints to measure the overall body movement. Lots of
body movement may indicate restlessness, which can be an indication for low
engagement [7]. In addition to that, we also considered the amount of
gesticulation an individual performs, as it plays an important role in
nonverbal communication [15] [6].

Finally, we also covered some form of conversation dynamics. Turn-taking and
vocal cues play an important role throughout a conversation [13]. During a
conversation, the interlocutors usually alternate their speaking turns.
Therefore we determine the interlocutor that is currently holding the turn, by
considering the general voice activity of the interlocutors. This allows us to
draw conclusions about the overall involvement of the individuals during the
conversation. An overall low voice activity may imply a conversation with low
engaged interlocutors.

###  3.2 Engagement Model

Based on the feature set introduced in subsection 3.1 we trained a simple
feedforward neural network with two dense layers for the recognition of low
and high engagement. For training the network we used the NoXi database [5].
It provides dyadic novice-expert conversations. We decided on the NoXi corpus
since it contains multi-modal multi-person interaction data and its
transferability to social coaching scenarios. Moreover, the setup of the
corpus allowed for both engaging as well as non-engaging interactions.

Figure 2: Confusion matrix of the neural network for the recognition of low
and high conversational engagement (test set).

A total of 19 sessions of the NoXi corpus have been annotated regarding
conversational engagement resulting in 10.5 hours of training data. The data
has been randomly split into training and test sets, so that no sample of the
same participant is present in the training and the test set. The training set
included 13 sessions and contained 6.8 hours of data. The rest was allocated
to the test set. Figure 2 displays the confusion matrix of the classifier for
the test set.

###  3.3 Counterfactual Features

In a next step, to be able to give recommendations on how the user should have
behaved to appear more engaged, we apply a counterfactual explanation
generation algorithm, i.e., we aim to modify the input feature vectors that
were classified as _low engaged_ in a way that the classifier would change
it’s decision to _high engaged_. As described in Section 1, the
recommendations that we aim for can be seen as counterfactual explanations for
the engagement model presented in Section 3.2. To generate these
counterfactual feature vectors, we used an adversarial learning approach. In
prior work, Mertes et al. [16] presented their _GANterfactual_ architecture,
which extended the CycleGAN framework [26], which is an adversarial approach
to domain translation, with further modifications that support the
architecture in transforming original samples to counterfactual samples that
are classified in a different way by a specific decision system to be
explained. To this end, they incorporated the classifier into the training
process of their CycleGAN-driven counterfactual explanation system via an
additional loss function component. For our system, we built a network
architecture adapted from the GANterfactual framework, which was originally
implemented for generating counterfactual explanations in the image domain.
The use of the GANterfactual framework has multiple benefits for the
recommendation quality: Firstly, the cycle-consistency loss that is an
integral part of CycleGANs forces that the learned transformation is minimal,
i.e., only relevant features are changed. In the context of recommendation
generation, this implies that the generated behavioral recommendations are
highly personalized. Secondly, the adversarial loss component that is part of
every GAN architecture leads to highly realistic results. Thus,
recommendations are not drawn from highly exaggerated or oversimplified
feature vectors. Thirdly, the counterfactual loss introduced by Mertes et al.
enforces that the counterfactual explanations (in our case, the behavioral
recommendations), are valid. As the engagement model that we used for our
system works with feature vectors with no spatial relations between the single
features, we replace the convolutional blocks of the original architecture
with fully connected blocks. Further, the input layer was adapted to fit the
feature representations that we also use for the engagement classifier. The
rest of the architecture, as well as the training procedure, was taken from
the original GANterfactual framework. For specific technical details, please
refer to our implementation.111Our implementation is available at
https://github.com/hcmlab/FeatureFactual. For the GAN-training, we relied on
the NOXI dataset, which we also used for training the engagement classifier.
Thus, the adversarial framework learns to convert feature vectors that show
low engagement to feature vectors that show high engagement.

###  3.4 Textual Recommendations

After generating the counterfactual feature vectors we compare them to the
original feature vectors that represent the shown nonverbal behavior.
Depending on the demanded detail of feedback we return the features that had
undergone the greatest value transformation. After identifying the most
meaningful counterfactual features we convert them into textual feedback. For
this purpose, we discretize the features based on a defined textual template.
For example, the feature representing the overall activity of the head gets
translated into ”try to keep your attention on your interlocutor” or ”try to
use more nonverbal feedback” depending on the present feature value. The
amount of discrete classes varies for different features and can easily be
adjusted depending on the given use case. The generated feedback is provided
verbally to the user by the virtual coach inside the job interview training
environment. An example of a recommendation provided by the virtual coach is
displayed in Figure 3.

##  4 Pilot Study

The present pilot study’s goal was to get preliminary insights about the
assessment of a possible job interview training applying GAN driven
recommendations. We used a mixed-methods design, combining questionnaires and
a semi-structured interview. The study was conducted in January 2022.

###  4.1 Method

####  4.1.1 Participants.

We gathered data from 12 volunteering student participants (7 female, 5 male).
Participants’ age was between 21 and 29 years (M = 23.83, SD = 2.66). On
average, participants attended 4.33 job interviews (SD = 2.74; Min = 1; Max =
10) prior to the study. Two of them had already experience with job interview
trainings, three with virtual agents.

####  4.1.2 Procedure and Material.

In this pilot study, the experimenter and participant met in a video call.
After agreeing to the consent form, the experimenter explained the background
of the study and presented videos of our job interview training system. For
the videos, we used a multi-modal job interview role-play dataset [24] to
create behavioral feedback. In that dataset, participants were confronted with
a job interview conducted either by an interactive social agent or a human
interviewer. Participants were recorded with the MS Kinect2. We used 5
sessions with the human interviewer as input to our job interview training
system. The resulting recommendations were then rendered into a video (Fig. 3)
that was shown to the participants. The participants saw the part of the job
interview training in which the trainee gets the individual feedback from the
virtual coach after having a mock job interview. The coach first presents the
recorded part of the job interview and gives the recommendation afterwards
verbally. Participants were asked to imagine that they were the trainees using
the training to practice a job interview. Next, participants filled in the
questionnaires. Then, the semi-structured interview was held. In the end, the
experimenter thanked the participants for their participation. The whole
procedure took around 25 minutes.

Figure 3: Coach giving the recommendation after the mock job interview.

####  4.1.3 Measurements.

Demographics included age, sex, job interview experience, and job interview
training experience. Usefulness was measured with the usefulness scale of the
MeCUE [17]. It contains three items. Cronbach’s Alpha was .92. Transfer
motivation was measured using four items adapted from [23] covering whether
training lessons learned will be useful in upcoming situations: “I believe
that my performance in job interviews will improve if I apply the knowledge
and skills I have acquired with training.”, “It is unrealistic to believe that
mastering the training content can improve my performance in job interviews.
”, “I can apply skills and knowledge acquired from job interview training to
my daily life.”, “I feel like after the training I could apply the behavior
very well. ”. Cronbach’s Alpha was .90. Feedback Quality was measured with
four self constructed items: “I felt the feedback was accurate.”, “I would
have given similar feedback.”, “I feel like the feedback is helpful.”, “I
don’t think the computer can give me accurate feedback.”. Cronbach’s Alpha was
.87. All questionnaire items were answered on a 7 point scale ranging from 1
(strongly disagree) to 7 (strongly agree).

The Semi-structured interview covered six areas: 1) general impression, 2)
persona, 3) other possible use-cases, 4) suggestions for improvement, 5)
intention for further use, and 6) added value.

###  4.2 Results

####  4.2.1 Questionnaires.

In the three questionnaires, the following descriptive data was found:
Usefulness (M = 4.72, SD = 1.17); Transfer motivation (M = 4.92, SD = .94);
Feedback (M = 4.60, SD = 1.26).

####  4.2.2 Semi-structured interviews.

The answers gathered in the semi-structured interview were analyzed and
categorized for each of the six areas separately:

1) Regarding the General impression, participants mentioned six times that the
recommendations were useful / feasible (e.g., “Simple tips that were easy to
implement, but have a big impact.”) or comprehensible (2x). Three participants
mentioned that the recommendations were too unspecific. Once each was
mentioned that the recommendations are not useful (“Would prefer feedback on
the content of my answer. Job interview is too stressful for me such that I
could focus on non-verbal behavior.”) and too obvious (“If I saw myself in the
video, I would have known that I have to improve the recommended behaviors.”).

2) Participants described the persona as someone with a wish to improve (7
namings) that is open for new thing (3 namings), career oriented (2 namings),
young (2 namings), self reflective (2 namings) or non-self reflective (1
naming).

3) As other possible use-cases participants named training to improve
communication skills in general (8 namings) and for more specific groups, like
patients with anxiety disorders or people with social phobias. The named also
other possible situations like preparing for challenging employee appraisals,
conflict resolution dialogs, or other high stakes situations. Another named
use-case was public speaking (4 namings).

4) Participants mentioned seven times that they would like to have more
specific recommendations, e.g. “The agent could say something like: Nonverbal
feedback is nodding, for example.” Moreover, they thought that recommendations
based on the content of the answers would be helpful (2 namings). Also, some
participants noted that the agent could be improved (3 namings), like using a
more empathic voice. One participant noted that an interactive training mode,
where you practice recommendations directly and get instant feedback would be
helpful.

5) Intention for further use was indicated by 9 participants. Three could not
imagine using the training.

6) The added value of the training was for most of the participants that the
recommendations are given directly on a specific behavior shown in a specific
situation during the job interview. Moreover, one participant mentioned that
the training was especially helpful as it gives a low-threshold possibility to
practice job interviews that could be offered by agencies supporting people to
find employment. One other participant said that having an agent instead of a
human giving recommendations decreases the feeling of being judged for
mistakes.

####  4.2.3 Recommendation generation

As described in subsection 3.3 we incorporated a classifier for the
recognition of engagement into the training process of the GAN via an
additional loss function component. In order to verify the validity of our
approach, we examined whether the counterfactuals generated by the GAN are
modifying the features that the engagement classifier identified as important
for the classification of low and high engagement. For this evaluation, we
used five sessions of the multi-modal job interview role-play dataset [24]
that have also been used in section 4 and extracted the importance scores of
every feature in regard to the model’s classification with LIME [22]. Next, we
calculated the absolute value change of how much each feature has been
modified by the counterfactual transformation. Afterwards, we calculated the
Pearson Correlation Coefficient between the importance scores of every feature
and the absolute change of each feature, see Figure 4. High correlation scores
indicate that the counterfactual feature transformation is in line with the
corresponding importance of the feature. The more important a feature is for
the classification of a sample the greater also should be the change of the
feature in order to result in a different classification result. Seven
features showed a strong positive correlation (GZ_DR, AM_CR, HD_TH, DIST_RW,
YROT_LE, SDX_HD, SDXROT_HD), six features had a moderate positive correlation
(HD_AC, YROT_RE, XROT_RE, TN_HD, CONT_MOV, EN_HA) and two features presented
with a low positive correlation (DIST_LW, XROT_LE). Moreover, FO_RW had a
strong negative correlation, VAL_F showed a moderate negative correlation and
FO_LW had a weak negative correlation.

Moreover, we conducted a computational evaluation to investigate how well the
generated counterfactual features change the decision of the engagement
classifier. For this evaluation, we also used the multi-modal job interview
role-play dataset. We found that 96.49% of the generated counterfactual
feature vectors led to a different decision of the engagement model as the
original input features.

Figure 4: Pearson correlation between the absolute change of the feature
values and the LIME classification relevance scores for every feature. The
features are from left to right: Valence Face, Gaze behavior, Head activity,
Arms crossed, Head touch, X distance of left/right wrist and hip, Y rotation
left/right elbow, Y distance of left/right wrist and hip, X rotation
left/right elbow, Standard deviation head movement in X axis, Standard
deviation Head X rotation, Turn hold, Continuous movement, Gesticulation.

##  5 Discussion and Conclusion

We introduce a novel approach for generating textual nonverbal behavior
recommendations in job interview training environments. In a pilot study, we
presented the approach to participants. The results indicate that such
training could be helpful to prepare for job interviews successfully. The
recommendations given by the system were found to be helpful and
comprehensible, and transferable to other use cases. Moreover, most
participants noted that the proposed approach adds additional value to the
training by giving recommendations directly on a specific behavior in a
specific situation. Part of the underlying training system automatically
extracts situations that could be improved and displays them alongside the
recommendation presented by the virtual coach. However, the pilot study also
revealed that the recommendations should be more specific. Therefore, in
future work, the template used for discretizing the counterfactuals should be
extended to be more diverse and specific or use natural language processing to
generate textual recommendations from counterfactuals directly. The latter
would need additional annotation and training effort.

Moreover, we examined the validity of our GAN-driven recommendation generation
approach by calculating the Pearson correlation coefficient between the
absolute changes of the feature values after counterfactual transformation and
the importance of the features the classifier attributed to them regarding the
classification result. We showed that most of the features (15 out of 18
features) had a moderate to strong correlation, which emphasizes the validity
of the proposed approach. Only the two features corresponding to the relative
position and movement of the left wrist and the feature representing the
flexion of the left elbow presented a weak correlation.

Further, it is interesting to point out that the feature representing the
relative movement of the right wrist (FO_RW) has shown a strong negative
correlation. This means that the counterfactual suggests decreasing the
relative distance from the wrist to the rest of the body when the current
feature value is an indication for low engagement. The opposite is the case
when the current feature value indicates being highly engaged, here the
relative distance should be increased. This indicates that for the given job
interview data, the engagement classifier attributes a lower wrist distance
towards the body as appearing higher engaged. A similar case presented itself
for the valence of the face. For this feature, we found a moderate negative
correlation. For the valence of the face, the classifier interprets lower
valence values, meaning a more serious facial expression, as a sign for higher
engagement. This interpretation is most likely related to the dataset used for
training the classifier and the corresponding conversational engagement
annotations. Therefore, extending the used training data for both the
classifier and the GAN for future work makes sense. Especially the classifier
might benefit from more training data as the accuracy scores leave room for
improvement. Also, the current classifier only distinguishes between low and
high engagement. It would also be interesting to investigate the resulting
counterfactuals when using a more fine-grained representation for
conversational engagement. Further, we also investigated how well the
generated counterfactual features can change the decision of the engagement
classifier. Overall, 96.49% of the counterfactual feature vectors led to a
different decision of the engagement classifier as the original input
features. This indicates that our GAN-driven approach enables to generate
recommendations that, when being adopted, are consistently leading to a
perception of high engagement. The computational evaluation, as well as the
user study, indicate that the generated recommendations are valid and helpful
in the context of job interview coaching scenarios.

####  5.0.1 Acknowledgments.

This work presents and discusses results in the context of the research
project ForDigitHealth. The project is part of the Bavarian Research
Association on Healthy Use of Digital Technologies and Media (ForDigitHealth),
funded by the Bavarian Ministry of Science and Arts. Further, the work
described in this paper has been partially supported by the BMBF under
16SV8688 within the MITHOS project, and by the BMBF under 16SV8493 within the
AVASAG project.

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