76 research outputs found
Interaction-driven User Interface Personalisation for Mobile News Systems
User interfaces of mobile apps offer personalised experience primarily through manual customisation rather than spontaneous adaptation. This thesis investigates methods for adaptive user interfaces in the context of future mobile news apps that are expected to systematically monitor users' news access patterns and adapt their interface and interaction in response. Although mobile news services are now able to recommend news that a user would be likely to read, there has not been equivalent progress in personalising the way that news content is accessed and read. This thesis addresses key issues for the development of adaptive user interfaces in the mobile environment and contributes to the existing literature of adaptive user interfaces, user modelling, and personalisation in the domain of news in four ways. First, using survey methods it explores differences in how people consume and read news content on mobile news apps and it defines a News Reader Typology that characterises the individual news consumer. Second, it develops a method for monitoring news reading patterns through a deployed news app, namely Habito News, and it proposes a framework for modelling users by analysing those patterns; machine learning algorithms are exploited selectively in the analysis. Third, it explores the design space of personalised user interfaces and interactions that would be tailored to the needs and preferences of individual news readers. Finally, it demonstrates the effectiveness of automatic adaptation through Habito News, the prototype mobile news app that was developed, which systematically monitors users' news reading interaction behaviour and automatically adjusts its interface in response to their news reading characteristics. The results indicate the feasibility of user interface personalisation and help shape the future of automatically changing user interfaces by systematic monitoring, profiling and adapting the interface and interaction
User Interface Personalization in News Apps
ABSTRACT News is increasingly being accessed on smartphones and tablets, establishing mobile news reading as one of the most popular activities on mobile devices. News reading is also a very individual activity with marked differences in the way people read and access the news, however, news apps have limited personalization. In this paper, we approach news personalization as a two-dimensional problem. We discuss news personalization in terms of 'what' content is delivered to the user and 'how' that content is consumed. We present our approach towards user interface personalization in news apps and we conclude that news content recommendation and user interface personalization should co-exist in news apps
Our Nudges, Our Selves: Tailoring Mobile User Engagement Using Personality
To increase mobile user engagement, current apps employ a variety of
behavioral nudges, but these engagement techniques are applied in a
one-size-fits-all approach. Yet the very same techniques may be perceived
differently by different individuals. To test this, we developed HarrySpotter,
a location-based AR app that embedded six engagement techniques. We deployed it
in a 2-week study involving 29 users who also took the Big-Five personality
test. Preferences for specific engagement techniques are not only descriptive
but also predictive of personality traits. The Adj. ranges from 0.16 for
conscientious users (encouraged by competition) to 0.32 for neurotic users
(self-centered and focused on their own achievements), and even up to 0.61 for
extroverts (motivated by both exploration of objects and places). These
findings suggest that these techniques need to be personalized in the future.Comment: 10 pages, 1 figure, 2 table
A Method for Generating Dynamic Responsible AI Guidelines for Collaborative Action
To improve the development of responsible AI systems, developers are
increasingly utilizing tools such as checklists or guideline cards to ensure
fairness, transparency, and sustainability. However, these tools face two main
challenges. First, they are static and are not meant to keep pace with the
latest responsible AI literature and international standards. Second, they tend
to prioritize individual usage over fostering collaboration among AI
practitioners. To overcome these limitations, we propose a method that enables
easy updates of responsible AI guidelines by incorporating research papers and
ISO standards, ensuring that the content remains relevant and up to date, while
emphasizing actionable guidelines that can be implemented by a wide range of AI
practitioners. We validated our method in a case study at a large tech company
by designing and deploying a tool that recommends interactive and actionable
guidelines, which were generated by a team of engineers, standardization
experts, and a lawyer using our method. Through the study involving AI
developers and engineers, we assessed the usability and effectiveness of the
tool, showing that the guidelines were considered practical and actionable. The
guidelines encouraged self-reflection and facilitated a better understanding of
the ethical considerations of AI during the early stages of development,
significantly contributing to the idea of "Responsible AI by Design" -- a
design-first approach that considers responsible AI values throughout the
development lifecycle and across business roles.Comment: 26 pages, 5 figures, 4 table
APPS 2021: Third International Workshop on Adaptive and Personalized Privacy and Security
The work has been partially supported by the EU Horizon 2020 Grant 826278 “Securing Medical Data in Smart Patient-Centric Healthcare Systems” (Serums), and by a new European project, TRUSTID - Intelligent and Continuous Online Student Identity Management for Improving Security and Trust in European Higher Education Institutions, which is funded by the European Commission within the Erasmus+ 2020 Programme.The Third International Workshop on Adaptive and Personalized Privacy and Security (APPS 2021) aims to bring together researchers and practitioners working on diverse topics related to understanding and improving the usability of privacy and security software and systems, by applying user modeling, adaptation and personalization principles. Our special focus in 2021 is on challenges and opportunities related to the Covid-19 outbreak, more specifically on ensuring security and privacy of sensitive data and secure user interactions in online systems. The third edition of the workshop includes interdisciplinary contributions from Belgium, Cyprus, Germany, Greece, Portugal, the Netherlands, and United Kingdom, that introduce new and disruptive ideas, suggest novel solutions, and present research results about various aspects (theory, applications, tools) for bringing user modeling, adaptation and personalization principles into privacy and systems security. This summary gives a brief overview of APPS 2021, held online in conjunction with the 29th ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP 2021).Postprin
Streetonomics: Quantifying culture using street names
Quantifying a society's value system is important because it suggests what
people deeply care about -- it reflects who they actually are and, more
importantly, who they will like to be. This cultural quantification has been
typically done by studying literary production. However, a society's value
system might well be implicitly quantified based on the decisions that people
took in the past and that were mediated by what they care about. It turns out
that one class of these decisions is visible in ordinary settings: it is
visible in street names. We studied the names of 4,932 honorific streets in the
cities of Paris, Vienna, London and New York. We chose these four cities
because they were important centers of cultural influence for the Western world
in the 20th century. We found that street names greatly reflect the extent to
which a society is gender biased, which professions are considered elite ones,
and the extent to which a city is influenced by the rest of the world. This way
of quantifying a society's value system promises to inform new methodologies in
Digital Humanities; makes it possible for municipalities to reflect on their
past to inform their future; and informs the design of everyday's educational
tools that promote historical awareness in a playful way.Comment: 17 pages, 6 figures, 2 table
FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing
How can we ensure that Ubiquitous Computing (UbiComp) research outcomes are
both ethical and fair? While fairness in machine learning (ML) has gained
traction in recent years, fairness in UbiComp remains unexplored. This workshop
aims to discuss fairness in UbiComp research and its social, technical, and
legal implications. From a social perspective, we will examine the relationship
between fairness and UbiComp research and identify pathways to ensure that
ubiquitous technologies do not cause harm or infringe on individual rights.
From a technical perspective, we will initiate a discussion on data practices
to develop bias mitigation approaches tailored to UbiComp research. From a
legal perspective, we will examine how new policies shape our community's work
and future research. We aim to foster a vibrant community centered around the
topic of responsible UbiComp, while also charting a clear path for future
research endeavours in this field
Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing
The field of mobile, wearable, and ubiquitous computing (UbiComp) is
undergoing a revolutionary integration of machine learning. Devices can now
diagnose diseases, predict heart irregularities, and unlock the full potential
of human cognition. However, the underlying algorithms are not immune to biases
with respect to sensitive attributes (e.g., gender, race), leading to
discriminatory outcomes. The research communities of HCI and AI-Ethics have
recently started to explore ways of reporting information about datasets to
surface and, eventually, counter those biases. The goal of this work is to
explore the extent to which the UbiComp community has adopted such ways of
reporting and highlight potential shortcomings. Through a systematic review of
papers published in the Proceedings of the ACM Interactive, Mobile, Wearable
and Ubiquitous Technologies (IMWUT) journal over the past 5 years (2018-2022),
we found that progress on algorithmic fairness within the UbiComp community
lags behind. Our findings show that only a small portion (5%) of published
papers adheres to modern fairness reporting, while the overwhelming majority
thereof focuses on accuracy or error metrics. In light of these findings, our
work provides practical guidelines for the design and development of ubiquitous
technologies that not only strive for accuracy but also for fairness
The State of Algorithmic Fairness in Mobile Human-Computer Interaction
This paper explores the intersection of Artificial Intelligence and Machine
Learning (AI/ML) fairness and mobile human-computer interaction (MobileHCI).
Through a comprehensive analysis of MobileHCI proceedings published between
2017 and 2022, we first aim to understand the current state of algorithmic
fairness in the community. By manually analyzing 90 papers, we found that only
a small portion (5%) thereof adheres to modern fairness reporting, such as
analyses conditioned on demographic breakdowns. At the same time, the
overwhelming majority draws its findings from highly-educated, employed, and
Western populations. We situate these findings within recent efforts to capture
the current state of algorithmic fairness in mobile and wearable computing, and
envision that our results will serve as an open invitation to the design and
development of fairer ubiquitous technologies.Comment: arXiv admin note: text overlap with arXiv:2303.1558
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