=Paper=
{{Paper
|id=Vol-3712/paper6
|storemode=property
|title=Explainability Challenges in Continuous Invisible AI for Self-Augmentation
|pdfUrl=https://ceur-ws.org/Vol-3712/paper6.pdf
|volume=Vol-3712
|authors=Dinara Talypova,Philipp Wintersberger
|dblpUrl=https://dblp.org/rec/conf/mum/TalypovaW23
}}
==Explainability Challenges in Continuous Invisible AI for Self-Augmentation==
https://ceur-ws.org/Vol-3712/paper6.pdf
Explainability Challenges in Continuous Invisible AI
for Self-Augmentation
Dinara Talypova1,2,∗,† , Philipp Wintersberger1,2
1
University of Applied Sciences Upper Austria, Hagenberg, Austria
2
TU Wien, Vienna, Austria
Abstract
Despite the substantial progress in Machine Learning in recent years, its advanced models have often
been considered opaque, offering no insight into the precise mechanisms behind their predictions.
Consequently, engineers today try to implement the explainability factors into the developed models,
essential for trust and adoptancy of the system. Still, there are several blocks in Explainable Artificial
Intelligence (XAI) research that cannot follow the standard design methods and guidelines for providing
transparency and ensuring maintaining human objectives. In this position paper, we attempt to chart
various AI blocks from the perspective of Human-Computer Interaction field and identify potential gaps
requiring further exploration. We suggest three-level dimension classification: relations with humans
(replacing vs augmenting), interaction complexity (discrete vs. continuous), and the object of application
(external world or users themselves).
Keywords
XAI, HCI, Continuous AI, Seamless Technology, Attention Management System
1. Introduction
Until these days, Artificial intelligence (AI) maintains a nuanced relationships with humans,
oscillating between a potential replacement of them to a technology that can augment and
expand human capabilities. As smart technologies become more prevalent in our daily routines,
the necessity for a smooth integration between humans and AI becomes evident ([1, 2, 3, 4, 5, 6]
just to name a few). This involves achieving a balanced incorporation of technology in our
day-to-day activities, recognizing its advantages while also being mindful of potential challenges
and unintended consequences.
The common believe is that some of the issues could be uplifted by focusing on AI explain-
ability [7, 8, 9]. Explainable Artificial Intelligence (XAI), a field that is focused on interpreting
machine decisions, takes its roots with the advent of AI systems already half a century ago
[10, 11, 12]. The shift from rule-based systems to modern deep learning has increased the diffi-
culty of interpreting AI decisions, moving beyond the simpler applied rules scenarios. Current
efforts in XAI involve the attempts to open AI’s ”black box” by enhancing transparency and
providing clear explanations for AI predictions, like justifying flagged fraudulent transactions
or selection processes in recruitment.
MuM’23 Workshop on Interruptions and Attention Management: Exploring the Potential of Generative AI, 2023, Vienna,
Austria
Envelope-Open dinara.talypova@fh-hagenberg.at (D. Talypova); philipp.wintersberger@fh-hagenberg.at (P. Wintersberger)
Orcid 0000-0002-6612-8061 (D. Talypova); 0000-0001-9287-3770 (P. Wintersberger)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Table 1
AI research blocks from an HCI research perspective
Replacing Humans Augmenting Human Capabilities
User as an agent User as an object
• Image Recognition apps
•Automated Customer Support (e.g., user apps for plant/bird detection);
•Physical Activity Trackers
Discrete AI (e.g., chatbots); • Photoshop Generative Fill;
(with immediate reminders)
•Automated Stock Trading • Clinical Diagnostics support;
• Google Translate
•Attention Management Systems;
• (Advanced) Google Assistant; • AI-enhanced sleep app;
•Self-Driving Cars; • Industrial robots; •Diabetes App;
Continuous AI
•Industrial robots • Traffic Management Systems; •Personalized News Feed;
• AI-driven Investment Portfolios •“Smart” Fridge
(i.e., monitoring diet & ordering food)
Moreover, the XAI challenge extends beyond revealing the internal inference processes of
AI algorithms — this is also important how the insights and explanations are communicated
to end-users. Human-Computer Interactions (HCI) researchers are concerned with ensuring
that the explanations are not only technically accurate but also usable, effective, and satisfying
for the people who interact with AI systems [13, 14, 15, 16]. This entails considering different
approaches based on user profiles, be they professionals utilizing the system (e.g., medical
practitioners), those affected by the system (e.g., patients), or developers themselves seeking
algorithm improvement. In summary, XAI’s role in HCI is twofold, aiming both to foster user
trust (that is, how well the insights could be delivered) and reinforce the reliability of AI systems
(that is, how reliable the conclusions are). As nicely formulated by Diefenbach et al., ”we must
be convinced that the other is capable of handling the task and that the other means well.” [17]
In this position paper, we attempt to chart various AI blocks from the perspective of HCI
research in explainability and identify potential gaps requiring further exploration. We believe
that despite long XAI research history, the field is unbalanced: while some categories of AI got
more attention from XAI perspective, the others remain in shadow. Therefore, to highlight this
skew, there is a need to categorize AI systems along the dimensions affecting their utilization,
continuity, as well as the nature of interaction with the world and humans. We came up
with three-level dimensions: relations with humans (replacing vs augmenting), interaction
complexity (discrete vs. continuous), and the object of application (external world or users
themselves). The blocks with examples of AI technology implementation from the industry are
presented in Table 1.
2. Replacing Humans vs. Augmenting Human Capabilities
One perspective in the interaction debate revolves around whether smart technologies should
replace humans [18, 19], thereby freeing up time for more meaningful or enjoyable activities, or
whether AI technologies should operate in tandem with humans [20, 21, 22], offering support
and enhancement. The former angle is exemplified by applications like automated customer
support (e.g., chatbots), industrial automation, and self-driving vehicles, all aimed at automating
tasks to potentially allow individuals to engage in more creative or self-developmental pursuits.
This still assumes a clear level of system transparency and interpretability of the outcome, so
humans monitoring the smart machines could trace back the solution process [23]. From the
XAI point of view, this approach may involve specific reports of what have been done and how
[24]. However, under this perspective, the XAI goal is not just explainability of the results but
rather reliability of the system, i.e., the trust that system can exhibit the same and expected
behavior over time [25].
In contrast, proponents of the augmentation approach contend that machines should collab-
orate with people to reinforce human capabilities. Thus, Autor et al. [26] argue that typical
job responsibilities are composed of task bundles; some of the tasks within these bundles are
more amenable to automation than others. This view inclines that AI will not displace humans
and their jobs entirely; instead, it will be used to enhance particular facets of tasks, promoting
effective collaboration between humans and AI (HAIC) [27].
It has already been demonstrated that HAIC is more efficient in diverse types of tasks
compared to humans’ or AI performance in isolation [20, 21, 28, 29]. In such tandem, each
party is responsible for the task in the bundle that it does better. In response to the this belief,
some academic conversations are shifting towards enhancing human capacities through AI
integration, redefining job roles for effective partnership, and transforming business processes
to align with these new collaborative models [30, 6, 31, 32]. Moreover, this concept does not stop
in efficiency. We envision that AI systems should not only automate tasks in order to increase
productivity, but also support, amplify, and extend human skills, creativity, and potential. That
is, some tasks will not be fully passed to AI also because of their enjoyable nature for humans
and feeling of autonomy.
Applications embodying skill augmentation approach include clinical diagnostic support,
writing assistance tools (e.g., Grammarly or ChatGPT), image recognition apps, and traffic
management systems. Advocates of this perspective believe that such cooperative relationships
will empower individuals, effectively elevating them to the status of ”superhumans.” As Dhiman
et al. reasonably claimed, “the assistants of the future will not only have to be trustworthy, respect
our privacy, be accountable and fair, but also help us flourish as human beings” [33]. However,
this approach requires additional emphasis on transparency and explainability of human-AI
interaction. Since humans stay in the process loop, understandable and meaningful cooperation
should be the key to success.
3. Discrete AI vs. Continuous AI
Within an amplifying humans vision of AI, we can distinguish AI as a tool (that enhances human
natural capabilities) or as a collaborator (that interacts with human and create fruitful synergy).
AI as a tool assumes that human explicitly uses the system to get (most probably) immediate
output, for instance a decision or a product. Consequently, interaction involving user input and
AI output occurs coherently in distinct stages, as is the case with, for example, conversational
agents, image recognition apps, AI writing support tools, etc. In these instances, AI systems
follow a sequential pattern: users initiate the process by issuing a command, which is then
interpreted by the system to generate a response. We call this type of interaction - Discrete AI.
While human-centered XAI has been extensively studied in the HCI field in the last decades
[34, 8, 35, 14, 9], the big scope of the research has concentrated in the context of discrete AI
[16], i.e., decision-making in situ. At the same time, contemporary research must adapt to
increasingly diverse and dynamic usage scenarios, where, for instance, user input and machine
output seamlessly unfold in parallel [36]. That is, AI as an agent that collaborates (explicitly or
implicitly) with the human being for their needs. These scenarios encompass applications like
Traffic Management Systems, smart homes, AI-driven Investment Portfolios, Self-Driving Cars,
and Industrial robots. They are ”intermittent, continuous, and proactive” [36]. We call this type
of interaction - Continuous AI.
Continuous AI is defined as ’the extended and seamless synergy between intelligent systems and
human users over extended periods of time’ [37]. This category of AI underscores the potential
of smart technology to provide support, assistance, and enhancement across various real-life
contexts, provided that we can ensure user interactions remain non-disruptive and do not
overburden cognitive resources. However, due to its proactiveness and continuous nature,
very soon this technology becomes unnoticeable, or in the terminology of [38], invisible. That
is, critical decision points are challenging to pinpoint, as the consequences of AI decisions
accumulate gradually, making it difficult to identify a clear ’point of no return.’ Therefore, on the
one hand, seamless continuous technology offers benefits by simplifying tasks and alleviating
the user from cognitive load. Yet, on the other hand, it introduces a degree of opaqueness,
potentially leading to feelings of uncertainty and diminished autonomy. In this context, the
importance of XAI becomes even more pronounced.
An in-depth examination reveals that current methods and guidelines, initially designed to
facilitate interactions with AI systems, often prove inadequate and occasionally counterproduc-
tive when applied to continuous usage scenarios [37]. For instance, conventional evaluation
methods often rely on assessing compliance and reliance rates to gauge user interactions with AI
or automated systems [39, 40, 41]. However, this approach is most effective when interactions
can be modeled as a series of isolated, subsequent trials, allowing users to retrace AI decisions
to specific events. It is less suitable for continuous and parallel interactions. Furthermore,
designed feedback mechanisms sometimes conflict with the seamless nature of continuous AI.
In continuous AI, the technology aims to operate on the background to achieve its objectives,
as in the example of Attention Management Systems (AMS) - technology that intelligently
postpones user’s notifications based on human’s cognitive load to cope with disruptions [42, 43].
If the AMS always reaches the user with information explaining delayed notification, there will
be no meaning in this system as it interrupts the user by trying to intelligently delay message.
As a result, there is a pressing need to develop fresh guidelines and evaluation methods tailored
to the description and assessment of Continuous AI scenarios.
4. ”User as an agent” vs. ”User as an object”
Guerrero et al. define the concept of augmented humanity as ”a human–computer integration
technology that proposes to improve capacity and productivity by changing or increasing the normal
ranges of human function through the restoration or extension of human physical, intellectual and
social capabilities” [44]. Unfortunately, in this definition, the grade of intrusion, i.e., whether
the restoration/extension of human capabilities realised within one’s body or through human-
computer synergy, is not defined. Still, within the realm of our discussion, it is more relevant to
differentiate the level of effect on human agency rather than embodied vs. extended nature in
the expression human-computer integration.
Numerous discussions on human-machine interactions revolve around a balanced and explicit
partnership between an agent and a human. That is, technology serves a specific function to
assist in reaching the user’s goal, predominantly concentrated on data detached from the actor
(e.g., [45]). In other words, a human represents an actor, (in most cases) an equal contributor to
the interaction process, whether AI-system is a writing assistant that shapes the typed text for
the industry standard, or an industrial robot that partners with the human in the manufacture.
When the user becomes the object of AI work in the context of self-augmentation, additional
challenges come to the fore. Notably, psychological nuances related to self-manipulation and
its acceptance factors become a central issue (see studies [46, 47]). For instance, the level
of intrusiveness is higher in using exoskeleton than cooperating with the robot in industrial
environment. When passing the decision about one’s own life to AI, concerns related to ensuring
AI’s transparency and alignment with desired needs and objectives rise up. Moreover, even if the
tasks of AI are aligned with our initial goals, are these goals still in place after our ”modification”?
We probably do not have that strong need to understand how or why the smart house changes
the temperature in rooms and starts self-cleaning at a specific time. As long as it works and
we do not freeze at home, it still makes our life easier, and that is what appears to matter most.
However, when it comes to manipulation of our own state, bigger questions arise. Within the
Discrete AI block, i.e., when the process of user input and AI output is sequential and each
request is initiated by human, individual has at least some control on technological influence
(that is, can trace one step back of where the result came from). While with Continuous AI, the
complexity of the technological integration make it difficult to assess the impact. According
to De Greef et al., the combination of increased complexity and undesirable machine behavior
should be considered a major risk [48]. If the AI wrongly concludes which support or when is
needed (or not needed), the potential benefits of adaptive automation turn into risks, elevated
by the hidden nature of this augmentation (see more on the topic of AI alignment [49]). The
examples here are a personalized news feed in social media that shows us specific news [3],
an intelligent sleep app that sends us to bed at a particular time and adjusts our sleeping
environment [50], or a smart habit-tracker that strategically manipulates what and how we
have to do [51].
Additionally, some scholars divide human augmentation by facets of human capabilities,
whether it is augmented senses (aka enhanced senses, extended senses), augmented action (e.g.,
motor augmentation, amplified force, and movement, speech input, etc.) or augmented cognition
(by detecting human cognitive state, using analytical tools to make a correct interpretation)
[52]. Within this division, the biggest shift in human agency is the last one — augmented
cognition. The principle of augmented cognition describes the symbolic human-AI integration
in a closed-loop system, where the system is designed to recognize the individual’s mental state
and the environmental context [53]. Thus, AI assistants could operate on behalf of humans,
based on their habits and preferences, proactively handling different types of tasks.
An instance of this nuanced interaction is found in Attention Management Systems (AMS),
which seamlessly operate in the background, collecting data from diverse sensors and gradually
influencing user behavior and interactions over time. The question of human awareness of
constant intentional nudging is extremely relevant here. In this context, transparency involves
communicating the simple presence or activation of technology, questioning whether it has
already initiated a process or if we are still in an unaltered environment.
It brings to the table the clear tension between smart and invisible versus transparent and
understandable technology. How can we find a balance between the seamless nature of the
continuous AI technology and keeping human in the loop of decision-making? How can
we control that the user still wants this technology and measure whether the system’s goals
continue to align with the user’s needs? What should be the explainability form of continuous
human-AI interaction for supporting trust and reliance in human augmentation? These are the
open questions that the modern research in XAI should aim to solve.
5. Conclusion
In this position paper, we made an attempt to chart AI systems through the lens of HCI ex-
plainability research. By identifying three dimensions — relations with humans, interaction
complexity, and the object of application — we underscored the inadequacy of universal ex-
plainability guidelines, particularly for seamless, continuous, and proactive AI systems oriented
towards self-augmentation. Consequently, we send out a call for the development of new
guidelines and evaluation methods specifically crafted to suit the characteristics of different AI
blocks.
Based on our exploration and the statements above, we provide a list of the most pressing
research questions around Explainable AI research that must be addressed to advance the field
effectively:
• Addressing the Gap in Continuous AI for Self-Augmentation: Highlighting the
need for focused research on the specific challenges posed by Continuous AI technologies
designed for self-augmentation, such as ensuring transparency and user control in highly
integrated and proactive systems, to balance the benefits of seamless interaction with the
necessity of maintaining individuals’ autonomy.
• Development of XAI Techniques for Continuous AI Systems: Establishing new
methods group to enhance transparency and human control in continuous AI presence,
ensuring explanations are comprehensible without abusing limited users’ attention.
• Enhancing Human-AI Collaboration & AI Alignment: Identifying ways to improve
human-AI collaboration to augment human capabilities effectively, focusing on empow-
ering individuals, preserving autonomy and ensuring the system still works in line with
human needs.
• Development of New Guidelines and Evaluation Methods: Creating flexible guide-
lines and evaluation methods that can accurately assess the effectiveness as well as
potential biases in explainability across varied and evolving usage scenarios in Continu-
ous AI for self-augmentation.
• Ethical Implications of Human-Augmentation AI Systems: Addressing the ethical
considerations of AI systems that act as agents of human augmentation, with particular
attention to the questions of free will, autonomy and consent, ensuring these technologies,
at minimum, avoid misleading and are developed responsibly.
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