=Paper=
{{Paper
|id=Vol-2863/paper-07
|storemode=property
|title=Towards Modeling AI-based User Empowerment for Visual Big Data Analysis
|pdfUrl=https://ceur-ws.org/Vol-2863/paper-07.pdf
|volume=Vol-2863
|authors=Thoralf Reis,Sebastian Bruchhaus,Binh Vu,Marco X. Bornschlegl,Matthias L. Hemmje
|dblpUrl=https://dblp.org/rec/conf/chiir/ReisBVBH21
}}
==Towards Modeling AI-based User Empowerment for Visual Big Data Analysis==
https://ceur-ws.org/Vol-2863/paper-07.pdf
Towards Modeling AI-based User Empowerment for
Visual Big Data Analysis
Thoralf Reisa , Sebastian Bruchhausa , Binh Vua , Marco X. Bornschlegla and
Matthias L. Hemmjea
a
University of Hagen, Faculty of Mathematics and Computer Science, 58097 Hagen, Germany
Abstract
User empowerment for information systems has the objective to increase the system usability
and the users’ self-confidence. This can be achieved through offering additional information
and adaptive user interfaces. Visual Big Data Analysis is an application domain that benefits
from user empowerment since skilled personnel are rare and the infrastructure is expensive.
The trending topic of AI is applied in industry and science to automate manual activities and
to support human users. Based on the AI2VIS4BigData reference model, this work proposes
an approach to utilize AI to empower users during their visual Big Data Analysis exploration
journey. The proposal comprises a two step AI-based user empowerment concept for visual
Big Data Analysis (insight extraction from Big Data and insight communication to end users)
as well as a research roadmap.
Keywords
User Empowerment, AI, Big Data, Visualization, Big Data Analysis, AI2VIS4BigData, Use
Cases, XAI
1. Introduction and Motivation
User empowerment for Information Systems (IS) has been discussed for some time
now [1, 2, 3]. It has the objective to maximize value generation through a symbiotic
relationship of the system user’s intellectual potential and the system’s capabilities. It
comprises methods and principles that aim at increasing the users knowledge level and
self-confidence to utilize as many of the opportunities an IS has to offer as possible
[2]. Examples comprise empowering users to tailor IS User Interfaces (UIs) to their
needs or to adapt the usage of the IS to be more goal-oriented and efficient. Knowledge,
information, and psychological aspects [2] are the key of user empowerment: users of an
IS are required to belief in their own skills and capabilities, they need to understand
what the objective of utilizing the IS is, and how it can be influenced to what degree [2].
BIRDS 2021: Bridging the Gap between Information Science, Information Retrieval and Data Science,
March 19, 2021, online
" thoralf.reis@fernuni-hagen.de (T. Reis); sebastian.bruchhaus@fernuni-hagen.de (S. Bruchhaus);
binh.vu@fernuni-hagen.de (B. Vu); marco-xaver.bornschlegl@fernuni-hagen.de (M. X. Bornschlegl);
matthias.hemmje@fernuni-hagen.de (M. L. Hemmje)
0000-0003-1100-2645 (T. Reis); 0000-0003-3789-5285 (M. X. Bornschlegl); 0000-0001-8293-2802
(M. L. Hemmje)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
67
In general, the principles of user empowerment are not restricted to IS of any specific
application domain. In [3] Bornschlegl et al. investigated end user empowerment for IS
in the popular application domain of Big Data Analysis. This investigation make sense
since there exist multiple challenges for this application domain [4] that are caused by
the huge amount of data (high volume), the high data inflow (high velocity), and the
high heterogeneity of the data (high variety) [5]; the challenges of high infrastructure
costs through processing and storage of Big Data, a lack of accessibility due to the high
dimensionality of the data, and insufficient testing and validation methods were mentioned
as the most-important challenges for Big Data Analysis in an expert roundtable workshop
in 2020 [4]. The high demand for skilled Big Data Analysis specialists [4] is another major
challenge that makes designing an IS for that application domain even more difficult.
User empowerment could address these challenges through adaptive UIs and intelligent
communication of relevant information in order to empower Big Data Analysis user
stereotypes, to use the system as efficiently and as successfully as possible.
IVIS4BigData is an existing standard for the design and implementation of IS for Big
Data Analysis [6]. It is reference model with user empowerment as key component [6].
IVIS4BigData models the process of Big Data Analysis as four processing steps from
data integration to the consumption of highly aggregated views and dashboards and
empowers experts and end users to interact with the system and intermediate results over
the whole process [6]. Therefore, the implementation of user empowerment is strongly
connected to the principles of Fischer and Nakakoji’s multifaceted architecture [3]. This
architecture consists of three layers [1]: a domain knowledge layer, a design creation
layer, and a feedback layer that connects domain knowledge layer and design layer [1].
This connection has the objective to empower users that specify and implement a design
through critical reflections, case-based reasoning, and simulation based on the domain
knowledge [1].
Fischer and Nakakoji explicitly decided not to design an expert system based on
Artifical Intelligence (AI) that utilizes AI to replace human users but to utilize it to
empower users for problem solving instead [1]. AI2VIS4BigData (Figure 1) is a reference
model [7] that has the objective to continue this idea by describing how AI can be utilized
for user-driven visual Big Data Analysis systems. This reference model is derived from
IVIS4BigData and describes how AI models themselves pass through the IVIS4BigData
processing steps (utilizing Big Data Analysis for designing AI models) as well as how
deployed AI models could support the process of Big Data Analysis (deployed AI models
for analytics, automation, and the UI) [7]. However, until now, AI2VIS4BigData lacks a
detailed description how AI can be utilized for empowering Big Data Analysis users.
The objective of this short paper is now to introduce a research approach and a
conceptual model that utilizes the ideas of Fischer and Nakakoji’s multifaceted architecture
[1] for the purpose of modeling AI-based user empowerment for visual Big Data Analysis
IS. The next sections comprise a description of this conceptual model and a research
roadmap before this short paper will conclude with an envisioned approach for its
validation as well as a brief summary with an outlook on future research directions.
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Figure 1: AI2VIS4BigData Reference Model for AI-based Visual Big Data Analysis [7]
2. Conceptual Modeling
There exist two main objectives of user empowerment for IS: the acquisition of additional
domain knowledge and increasing the user’s self-confidence in mastering the system’s
complexity [2]. This short paper proposes to interpret Fischer and Nakakoji’s multifaceted
architecture [1] for visual Big Data Analysis as follows in order to derive an approach for
AI-based user empowerment: the goal of design creation shall be the formulation and
execution of a fruitful Big Data Analysis that fulfills the user’s information demand; this
design creation requires the user to be self-confident and informed enough about potential
decisions (specification) and to actually realize the Big Data Analysis (construction);
insight about the data (semantics base) as well as experiences about successful or inefficient
workflows (catalog and argumentation base) support the user to be as self-confident
as possible. The resulting interpretation is visualized in Figure 2 and divided into the
two steps of Big Data insight extraction (step 1) and insight communication (step 2).
This interpretation is in agreement with Norman and Draper’s user-centered system
design approach in which they "focus on the substantive details and origins of the mental
models users construct for how computational systems work" [8] in order to bridge the
gap between user and the system. They proposed three criteria to be relevant for users
to understand IS: "internal coherence, validity, and integration of available and new
knowledge" [8].
Following the interpretation in Figure 2 and the criteria of integration new knowledge
for Norman and Draper’s user-centered system design [8], a potential way of introducing
AI-based user empowerment for the different user stereotypes in visual Big Data Analysis
is to utilize AI to extract the required Big Data insights and to transport them by utilizing
rules (predefined or symbolic AI) to the user. These insight can either be transported via
direct visual guidance (e.g., color highlighting of relevant data artifacts) or via indirectly
emphasizing the interaction with the system (e.g., reordering a context menu according
to data characteristics). The user story in Figure 3 exemplarily visualizes this process.
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Sp
Co
eci
ns
tru
fica
Big Data Analysis Big Data Analysis Big Data
cti
tio
User’s Mental Model Realization Analysis
on
n
Design
Simulation Feed-
Step 2: Communication
Case-based of Data Insights
back
Critics Reasoning
Domain
Inefficient Expert Successful System Big Data Knowledge
Workflows Knowledge Workflows Insight Insight
Step 1: Extraction of
Argumentation Catalog Semantics Big Data Insights
Base Base Base
Figure 2: Multifaceted Architecture [1] Interpretation for User Empowerment in Visual Big Data
Analysis
(a) (b) (c)
? !
AI Proce
ssing...
Figure 3: User Story for AI-based User Empowerment in Visual Big Data Analysis
Figure 3 captures a visual Big Data Analysis IS and a user that utilizes it at three points
in time. Starting with (a), an uncertain user visually explores Big Data by analyzing
graphical representations of the data in a traditional way. Without requiring further user
input, the IS executes predefined AI models in (b) that extract features and insight from
the data. In (c), the user is informed about this additional knowledge about the data
through visual highlighting of the data visualization and gains self-confidence for further
IS interaction. Thus user empowerment in context of this work means to enhance the
user’s self-confidence to interact with an IS in a sophisticated manner that lets him/her
exploit the system’s full potential.
In order to model the two steps of extraction of information from Big Data (step 1) and
of transporting this information to the user (step 2) in more detail, specific and practical
use cases need to be derived and integrated within a UI. Figure 4 translates the user story
from Figure 3 into a wireframe based on the IVIS4BigData UI implementation [6]. The
wireframe visualizes the exemplary use case of AI-based data hotspot detection. It shows
the IVIS4BigData UI in which multiple integrated and analyzed data collections are
presented to the user. Within this scene, the user can select one or multiple data collections
70
Toggle Expert Mode Toggle Fullscreen Help
IVIS4BigData
Home Data Analysis Visualization Perception Knowledge
Preprocessing
User Instructions
Configuration
Data Artifacts Knowledge
Preprocessed Data Name
Instances Name … AI
Data Collection 1
E Label Configuration I Data Collection 1 AI Edit K Insights:
E Instance Relationship
Method Configuration
I Data Collection 2 Edit K
Hotspot detected at
Active I Analyzed Data Collection 1 Edit K Data Sample 23
(anomalous high value in
Normalization Dimension 12).
Preprocess
Attribute Name 1 ..
Categorical Value
Figure 4: Wireframe of IVIS4BigData [6] and AI2VIS4BigData UI establishing AI-based User Empow-
erment
and apply an analysis method or visualize the data. In this example, AI2VIS4BigData
executes an AI model to detect hotspots within these data collections. After application of
this model and extraction of hotspot information, the identified hotspot is communicated
to the user via a pre-determined area for knowledge on the data artifacts located at the
right side of the wireframe.
3. Research Roadmap
The application, implementation, and validation of the derived conceptual model requires
specific application scenarios and target user groups, concrete ideas how the information
can be transported to these users, and a comprehensive evaluation strategy. For this
purpose, this paper proposes a three step research roadmap that is explained within the
following subsections.
3.1. Identifying User Empowering Use Cases
Beyond detecting hotspots within the data (Figure 4), there exists various application
scenarios for empowering Big Data Analysis users. The identification of such scenarios
can either be carried out in a theoretical and deductive or in a practical and inductive
way. This short paper proposes to combine both strategies to identify these scenarios
and to design further UI wireframes:
• Deductive approach: Identifying manual activities in visual Big Data Analysis
through examination of all manual activities that are required to go through all
processing steps of the theoretical IVIS4BigData reference model
• Inductive approach: Assessment of practical and relevant user challenges through
conducting a literature research or an expert survey
71
Since specification and implementations of these use cases will require further informa-
tion, the proposed approach contains two further steps that enable putting the use cases
into context:
• Derivation of a use case framework consisting of all relevant elements from AI2VIS-
4BigData and Fischer Nakakoji’s multifaceted architecture through extending the
existing IVIS4BigData use case framework [3]
• Contextualizing the use cases through modeling the relationships among each
other as well as the relationships to reference model elements within this use case
framework
3.2. User Empowerment with Explainable AI
The potential of user empowerment for visual Big Data Analysis as motivated in the
previous sections goes beyond enhancing the ability to draw conclusions from data
sections. AI systems should also qualify a certain degree of confidence in their results.
The collaborative approach to data processing with artificial and human actors requires
trust and "digital empathy" [9, 10]. The intuitive concept of trustworthiness relies on
predictability and hence some form of Explainable AI (XAI) [11].
A prevalent hypothesis concerning XAI suggests a trade-off between interpretability
and accuracy of models. Deep Learning (DL) is an example of "black box" models under
this assumption. Yet model-agnostic explanation methods like, e.g., LIME and SHAP and
those specific to DL like, e.g., GradCAM have put this notion somewhat into perspective
[12, 13, 14]. Different explanatory techniques can be combined to enhance their overall
efficacy [15]. Even more principled approaches to XAI like, e.g., Bayesian learning have
been incorporated into Machine Learning (ML) frameworks such as TensorFlow [16].
Enhanced transparency usually causes computational cost and cognitive load for the
user, but not all automated decisions require justifications. The users of ML models
therefore ought to be able to choose the appropriate kind of explanations as well as
the mode of their mediation. Decision support in this regard is itself a candidate for
automation by recommender systems. Data quality monitoring ought to be considered in
this context as well, because the performance of ML models corresponds to the quality
of its training data.
There are mainly two ways for conveying explanations, i.e. textual [17] and visual.
The latter overlaps extensively with statistical visualizations, e.g., the familiar line of
best fit of a simple linear regression model or graph plots of Bayes belief networks.
This work proposes that user stereotypes should be offered appropriate ML models and
explanation techniques along each step of AI2VIS4BigData. Context sensitive dialogue
systems are an obvious method for this. They should not interrupt the workflow but rather
offer reasonable defaults. Explanations of automated decisions can then be generated in
parallel to the actual ML process. Recommendation systems for the desired form of XAI
can help to minimize the cognitive load on user stereotypes. Visual explanations are to
be overlayed above the actual visualizations in an optional graphical layer. These textual
and visual explanations shall then complement the text in Figure 4 for the purpose of
72
user empowerment. A linear regression plot is a simplistic example in this context that
lets the user check the trained models’ performance at a glance.
3.3. Validation of the Conceptual Model
Since user empowerment for IS is strongly related to psychological aspects [2] and the
proposed conceptual model depends on the user’s mental model [8], a validation strategy
is challenging. In a nutshell, there are to potential ways to validate the proposed approach
for AI-based user empowerment:
1. Passive, observatory validation of the user’s mental model according to Norman
and Draper [8] through, e.g., measurement of the required time with and without
additional knowledge until the user successfully imagined how the IS can be utilized
for a certain Big Data Analysis task.
2. Active, interrogating validation of a certain user while being presented multiple
wireframes of the system.
This paper proposes to follow the second option in form of a cognitive walkthrough,
an evaluation technique that focuses on "how well [...] first-time use without formal
training" [18] of a system is. A cognitive walkthrough can be conducted with wireframes
and does not require a full implementation of the system. During this validation, an UI
or domain expert user is asked to fulfill a set of tasks for which he/she has to interact
with the system. The validation reaches a quantitative result through comparing the
user’s action with "a list of the correct actions required to complete each of these tasks"
[18] as well as qualitative results through interviewing the test user afterwards regarding
perceived challenges. For this, all IVIS4BigData processing steps can be passed through
with multiple wireframes for AI-based user empowerment examples.
4. Summary and Outlook
This short paper presents a research approach that enables modeling AI-based user
empowerment for visual Big Data Analysis through AI-based Big Data insight extraction
and communication of this insight to expert and end user stereotypes. This approach was
derived through interpretation of Fischer and Nakakoji’s multifaceted architecture [1] and
is based on the AI2VIS4BigData reference model. In addition, this paper introduced a
research roadmap of immediate next research objectives in order to employ the conceptual
model.
This research roadmap comprises the derivation of AI-based use cases for Big Data
insight extraction, the definition of an AI2VIS4BigData use case framework, the instantia-
tion of this use case framework to model the use cases, the investigation of XAI’s potential
for empowering end users, and the validation of the conceptual model for practical use
cases. In addition the outlook comprises detailed specifications and prototypical imple-
mentations of the conceptual model as well as looking into the differentiation between
inefficient and successful workflows. The latter enables a comprehensive application of
the multifaceted architecture within an IS.
73
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