=Paper=
{{Paper
|id=Vol-3828/ISWC2024_paper_11
|storemode=property
|title=The EASY-AI Symbology
|pdfUrl=https://ceur-ws.org/Vol-3828/paper11.pdf
|volume=Vol-3828
|authors=Alexis Ellis,Cogan Shimizu
|dblpUrl=https://dblp.org/rec/conf/semweb/EllisS24
}}
==The EASY-AI Symbology==
https://ceur-ws.org/Vol-3828/paper11.pdf
The EASY-AI Symbology
Alexis L. Ellis1,∗ , Cogan Shimizu1
1
Wright State University
Abstract
As artificial intelligence (AI) surges into the forefront of research and the lives of everyday people,
challenges in understanding and communicating how these systems operate are becoming more prevalent.
The need for a common language for AI systems that allows for multidisciplinary understanding and
communication is a prevalent topic within the field. In this work, we take the visual framework EASY-AI
and create a symbolic system that overlays the framework’s ontology to facilitate such communication
and understanding. Poster submission.
Keywords
Explainable AI, Human Machine Interaction, Human Factors Engineering, Symbology, Education
1. Introduction
As artificial intelligence (AI) moves into the spotlight of not only major research interests but also
its rapid implementation into our daily lives, challenges in understanding and communicating
these systems are becoming more prevalent. These challenges will only increase in complexity as
advancements in the state-of-the-art [1] continue to evolve. Therefore, constructing a common
language that can facilitate inclusive collaboration of all AI users, a fundamental understanding
of systems, and a means of explainability is becoming necessary. There are many ways we can
attempt to tackle the interpretability, communication, and explainability of AI, however, one
way is through a visual framework that utilizes symbols to represent these AI systems. The
utilization of symbols to convey information is not a new idea, with the use of symbols being
traced back from our earlier recorded history [2] to our present-day use of symbols in the form of
emojis, capable of conveying entire thoughts and feelings [3]. Through a visual framework that
utilizes symbols, we can break down AI systems ranging from the simplest systems to the higher
complexities we will encounter in the future. Our work of sEmantic And compoSable glYphs
to represent artificial intelligence systems (EASY-AI) [4] aims to provide such a framework,
combining ontology which facilitates in-depth understanding and a symbolic-driven visual
component that creates a more digestible representation of AI systems.
Posters, Demos, and Industry Tracks at ISWC 2024, November 13–15, 2024, Baltimore, USA
∗
Cogan Shimizu.
Envelope-Open ellis.177@wright.edu (A. L. Ellis); cogan.shimizu@wright.edu (C. Shimizu)
Orcid 0009-0000-8098-4262 (A. L. Ellis); 0000-0003-4283-8701 (C. Shimizu)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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2. Related Work
Work to develop a visual framework that represents and provides explainability, multidisci-
plinary collaboration, and transparency to AI is starting to surface in the field, with work like
The Boxology framework [5]. The boxology framework takes a broad approach using various
simple geometric shapes to represent parts of an AI system and its algorithms and arrows to
indicate the flow of data from one component to another. The work conducted in our framework
EASY-AI extends boxology by providing a deeper level of explainability with the implementation
of an underlying ontology. Initially, the ontology would provide standardization and constraints
to how the shapes in the boxology could interact using axioms derived from [6, 7] which can be
seen in the top right corner of figure 2. However, the issue in the organization and presentation
of systems became apparent when looking to fit the EASY-AI framework on a complex system
using only the boxology as the visual representation. Therefore, we have extended EASY-AI
by creating a symbology that can condense the visual representation of simple and complex
systems to aid in overcoming this challenge.
3. The EASY-AI Glyphs
The symbology of EASY-AI is inspired by the drag-and-drop symbolic widget interface of
the Orange Data Mining tool [8]. EASY-AI’s symbolic widgets will represent the different
instances of data, processes, and models that the boxology framework distinguishes, but these
new symbols can connect and collapse to lower the visual load of the user to reduce the
chances of inattentional blindness which a high visual load can cause [9]. Represented in
Figure 1 is each of the symbols that will be used in the next section 3.1 with a Use Case.
Beginning at the top left and moving right
we have the input-type-data, process-type-
train, model-type-statistical, and process-
type-deduce. Moving down to the next
row starting from the left side, moving
right is model-type-semantic, process-type-
reinforced-learning, input-type-symbol, and
output.
3.1. Use Case
Figure 1: The EASY-AI Glyphs.
To demonstrate how the glyphs from figure 1
work in the framework we have applied them
to the Roomba use case [10] which originally utilized the initial EASY-AI framework with the
boxology as the visual component depicted in the bottom right of figure 2. The Roomba use
case is not a very complex system to represent, however, from this visualization, it would not
be difficult to imagine how chaotic and confusing this framework can be if it were to attempt to
represent a highly complex AI system. This initial layer would be considered the foundational
layer where we have a more technical representation of AI systems. Using the new symbology,
we swapped the boxology representations of the Roomba as seen in the bottom depiction in
Figure 2: The EASY-AI Roomba Use Case
figure 2 to the symbology reconstruction on the bottom left of figure 2. To compose the amount
of visual information that the user ingests, the framework can then be collapsed if necessary, as
demonstrated in the top left depiction in figure 2. The framework is meant to be able to expand
or collapse as the user’s needs vary as well as how in-depth they wish to understand the system.
4. Conclusion
With the rapid advancements in artificial intelligence (AI) a way to explain, understand, and
communicate these systems is becoming a prevalent challenge not only between domain experts
but also to casual users. The EASY-AI framework is set to overcome these challenges by
providing a common language. Paired with EASY-AI’s underlying ontology for explainability,
we have now provided a simplistic and easily digestible way to communicate systems to one
another by adapting a symbology to the EASY-AI framework. While these symbols have not
undergone user testing, we hope to conduct such testing in the near future. Additionally, we
will implement the symbolic nesting for SNOOP-AI [11] in CoModIDE [12].
Acknowledgments
This work was partially funded by DAGSI/AFRL under award RX23-8, The Advanced Air
Mobility (AAM) grant, and NSF Award #2333532.
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