=Paper=
{{Paper
|id=Vol-3136/paper11
|storemode=property
|title=A human-centric AI-driven framework for exploring large and complex datasets
|pdfUrl=https://ceur-ws.org/Vol-3136/paper-11.pdf
|volume=Vol-3136
|authors=Maria Francesca Costabile,Giuseppe Desolda,Giovanni Dimauro,Rosa Lanzilotti,Daniele Loiacono,Maristella Matera,Massimo Zancanaro
|dblpUrl=https://dblp.org/rec/conf/avi/CostabileDDLLMZ22
}}
==A human-centric AI-driven framework for exploring large and complex datasets==
https://ceur-ws.org/Vol-3136/paper-11.pdf
A Human-centric AI-driven Framework for Exploring Large and
Complex Datasets
Maria F. Costabile1, Giuseppe Desolda1, Giovanni Dimauro1, Rosa Lanzilotti1,
Daniele Loiacono2, Maristella Matera2 and Massimo Zancanaro3
1
University of Bari Aldo Moro, via Orabona, 4, Bari, Italy
2
Politecnico di Milano, Via Ponzio, 34/5, Milano, Italy
3
University of Trento, Corso Bettini, 84, Rovereto, Italy
Abstract
Human-Centered Artificial Intelligence (HCAI) is a new frontier of research at the intersection
between HCI and AI. It fosters an innovative vision of human-centred intelligent systems,
which are systems that take advantage of computer features, such as powerful algorithms, big
data management, advanced sensors and that are useful and usable for people, providing high
levels of automation and enabling high levels of human control. This position paper presents
our ongoing research aiming to extend the HCAI framework for better supporting designers in
creating AI-based systems.
Keywords 1
Human-centered Artificial Intelligence, design framework, pattern
1. Introduction and motivation
The Human-Computer Interaction (HCI) community acknowledges Artificial Intelligence (AI) as
very valuable to create digital technology that can greatly empower people. At the same time, there is
still a lack of understanding on how to benefit from AI while preserving the system's reliability,
safeness, and trustworthiness for humans [21]. AI is usually conceived with a perspective on autonomy:
from autonomous behaviour to autonomous decision-making [17]. In several contexts, a high degree of
autonomy might be useful (albeit not without risks). Yet, a different approach might be to exploit AI to
support and facilitate human beings by augmenting (and valuing) human cognitive abilities rather than
replacing them.
By leveraging the new frontiers of research at the intersection between HCI and AI, we are starting
to work on foundational research for a new extension of AI, called Human-Centered Artificial
Intelligence (HCAI), by shifting the attention from an algorithm-focused view to a human-centered
perspective that requires HCI strategies of design and testing [22]. HCAI perspective ([21], [22]) aims
to propose methods to design new interaction paradigms that can amplify, augment, and enhance human
performance, in ways that make systems reliable, safe, and trustworthy.
A fundamental element in HCAI is the control by end users. As envisaged by a leading AI researcher
[18], a new relationship between humans and machines is needed to design machines that are “not just
intelligent but also beneficial to humans”. End users’ control could be fostered by defining AI models
and algorithms that can grant transparency to the system behavior, making it easy for the end users (and
all the stakeholders in general) to understand and trust the system's decisions, the so-called Model
Explainability [15]. Explainability is generally considered to highlight technical features characterising
the performance of AI models. Methods proposed in the literature to open black box models identify
Proceedings of CoPDA2022 - Sixth International Workshop on Cultures of Participation in the Digital Age: AI for Humans or Humans for
AI? June 7, 2022, Frascati (RM), Italy
EMAIL: maria.costabile@uniba.it (M. F. Costabile); giuseppe.desolda@uniba.it (G. Desolda); giovanni.dimauro@uniba.it (G. Dimauro);
rosa.lanzilotti@uniba.it (R. Lanzilotti); daniele.loiacono@poliba.it (D. Loiacono); maristella.matera@polimi.it (M. Matera);
massimo.zancanaro@unitn.it (M. Zancanaro)
ORCID: 0000-0001-8554-0273 (M. F. Costabile); 0000-0001-9894-2116 (G. Desolda); 0000-0002-4120-5876 (G. Dimauro); 0000-0002-
2039-8162 (R. Lanzilotti); 0000-0002-5355-0634 (D. Loiacono); 0000-0003-0552-8624 (M. Matera); 0000-0002-1554-5703 (M. Zancanaro)
© 2022 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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useful explainability strategies [11]. However, important issues remain open. Different scientific
communities address explainability from different perspectives. The explanations provided by the AI
community are primarily directed to AI specialists. The HCI community considers these approaches
inadequate since they are not meant for the end users, who are possibly domain experts but not AI
experts ([14], [23]).
Reliable user control on the system can be also achieved by granting an interactive manipulation of
the relevant parameters determining the system behaviour [19]. In the HCAI vision, user control and
system autonomy are not considered as opposing each other but rather as two dimensions to be
adequately calibrated when designing intelligent systems beneficial to people [21]. Users should be
enabled to take advantage of the power of the AI algorithms, but the importance of the knowledge that
users, as domain experts, possess must not be neglected. As an example, in [2] an ML-based tool to
visually retrieve medical images (tissue from biopsies) from past patients is presented. The tool supports
medical decisions with new patients, empowering the physician to cope with the search algorithm on-
the-fly, communicating what types of similarities are most important in different situations. This
interaction between the human and the system determines a step-wise-refinement that increases the
diagnostic utility of images found, as well as the user’s trust in the algorithm.
The opportunity for the users to modify the system behavior and adapt it to their needs (the so-called
End-User Development [1], [8]), possibly acting on the system's AI models, is crucial in the long term
for real empowerment in the use of AI systems. Meta-design principles [8] must be adopted to define a
methodological framework in which developers and AI specialists do not design a rigid system, rather
they provide a scaffolding environment where adequate model explanations can empower domain
experts to reconfigure algorithm outcomes through negotiation. In [12] the authors discuss Interactive
ML techniques enabling model reconfiguration through experts’ intervention in medical scenarios.
However, they also highlight the need for HCI methods to identify adequate interaction paradigms.
Despite the novel contributions mentioned above, a comprehensive framework able to support
designers in creating HCAI systems is still lacking. Our goal is to fill this gap by proposing a conceptual
framework and a pattern language for the design of a new class of tools with which human beings of
different levels of expertise can engage in interactive and iterative meaning-making activities for
exploring large and complex datasets. Specifically, the framework promotes i) a new notion of
explainability, which can be adequate for non-technical people, and ii) new paradigms for the
interaction between humans and systems that can enable a negotiation process that empowers the human
not only to understand the reasons that determine a specific system behaviour, but also to modify it
through iterative reconfigurations.
2. Three overarching strategies of interaction with AI systems
We aim to extend the HCAI conceptual framework for the design of a new class of tools with which
human beings of different levels of expertise can engage in interactive and iterative meaning-making
activities for exploring large and complex datasets. Three basic strategies of interaction with AI (ML-
based) systems will be explored. They are:
1. Clarification: means explaining directly the reason for the behaviour/decision of the AI
system. It implies strategies for different types of explanations as an alternative to the so-
called “black-box” of deep learning systems (e.g., [20]). The explainability It also involves
design patterns to communicate the level of certainty of the system suggestions, which are
often based on applying thresholds to computed probabilities (e.g., [3], [16]);
2. Negotiation: means reaching the system outcome through a sequence of iterative negotiation
steps driven by the interaction between the user and the AI system. It involves meta-
strategies and design patterns aimed at segmenting the decision process in progressive steps
and providing means to assess and recalibrate the request to the system (e.g., [2]);
3. Reconfiguration: means the possibility to trigger simply (within the reach of non-expert
users) the retraining of the AI model(s) on new examples. This may happen when the users
detect wrong suggestions by the systems or when new data should be incorporated into the
model after a negotiation phase (e.g., [12]).
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While each one of these strategies has been addressed by some other works, a comprehensive
framework that integrates them is still lacking. Also, while several validated patterns exist for standard
graphical user interfaces (e.g., [24]), there are still no comparable guidelines for novel paradigms of
Human-AI system interaction.
As one of the major outcomes, our research will define interaction-design patterns for HCAI. Patterns
are indeed a useful cognitive tool that captures human expertise to provide effective and reusable
knowledge [24] on how to manage specific aspects of a design. One of the main features of design
patterns is their uniform structure and specification format, which is composed of several items giving
the designer indications on the addressed problem, the proposed solution, the context in which the
pattern can be applied, the purposes and the benefits expected, and, eventually, examples of the
application of the selected pattern. Some of the authors of this position papers have a large experience
in the definition of patterns [4], [5], [10], [13].
3. The case study
The three broad strategies above will be experimented within one main case study in the medical
domain to derive grounded principles of human interaction with AI systems.
The case study will be in the challenging context of medical cytology, specifically by tackling the
problem of AI-supported microscopic analysis of cells contained in the nasal mucosa [6], [7]. We will
base our exploration on an existing tool called Rhino-Cyt [6] and we will design, deploy and assess new
interface prototypes and related interaction paradigms based on the current ML model and backend
system. Unlike what happens for example in the field of haematology, nasal cytology does not yet
benefit from a network of public or private laboratories that carry out in-depth analyses quickly and at
a low cost. Therefore, the whole diagnostic process is mainly based on direct observation under the
microscope that requires prolonged effort by domain experts. The modern scanning systems for
cytological preparations and the new affordable digital microscopes enable the design of software
systems to support the physician's activity. Rhino-Cyt encodes a deep neural network to automatically
identify and classify cells present on a nasal cytological preparation based on a digital image of the
preparation itself. Concerning the more standard approach to automating cell counting, Rhino-Cyt aims
to move from the current semi-quantitative estimation to a quantitative one, which is more precise and
valuable on a scientific level for standardisation, to catalogue cellular elements and get a more accurate
diagnosis in the shortest time. These changes may help in the more pervasive use of nasal cytology, a
diagnostic investigation that is yet not massively adopted by the new generation of physicians. Although
the tool already includes over 3,000 images, the main obstacle at present is the lack of data: some classes
are underrepresented and generate underfitting. In this respect, it is necessary to design algorithms that
can lead to the achievement of the final diagnosis through a process of successive steps, with the
intervention of the physician. In the current interface, the physician manually disambiguates all
unclassified or incorrectly classified cells. The physician can also intervene to correct the system’s
classification if it is regarded as incorrect. Yet, neither specific strategies are implemented at the
interface level nor in the general interaction with the system, therefore it is the full responsibility of the
physician to decide if and how to intervene.
With the aim of investigating different interaction strategies, we will propose several redesigns of
the Rhino-Cyt interface and, in general, of the interaction between the physician and the AI backend
along the broad strategies above. Through the assessment of these solutions with the domain experts,
we will distil lessons that can help identify patterns for the interaction with AI-enhanced systems.
4. Conclusions
The aim of our current research is to inquire about foundational aspects of Human-Centred AI and
its main goal is to deliver a general framework and a library of patterns for interaction with AI tools
meant as an extension of the Human-Centred AI framework.
The applied methodology is “research-through design” approach [26], which emphasises the
production of prototypes as vehicles for inquiring about foundational aspects of a research challenge.
This approach is commonly used in HCI while it is rarely used in AI. Other relevant outcomes of the
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project are a redesign of the existing system Rhino-Cyt and a set of specific lessons learned for AI in
medicine derived from our fieldwork with physicians and specialists in the field of cytology.
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