1,720,972 research outputs found
Reflection by Interaction: How Technology can Support Users in Reflecting on Personal Data
No full textToday, we are surrounded by devices that collect data about us. Think of your smartphone tracking screen time, phone pickups, and app usage. Or consider your smartwatch measuring your resting heart rate, stress levels, and sleep score. In academic literature, these are called Personal Informatics (PI) systems. These support people in gathering personally relevant information for self-reflection and self-knowledge.
Self-reflection offers benefits like enhanced self-insight, motivation for behavior change, and support for life changes. However, reflection often does not happen automatically and must be encouraged. Some people are more inclined to reflect, and it develops with age or training. Therefore, how users can be supported in reflection should be carefully considered when designing such systems.
The benefits of reflection are widely recognized in Human-Computer Interaction (HCI) research. Many studies have been conducted to understand how technology can promote reflection. However, at the start of my PhD research, questions remained: What strategies and techniques can we use to encourage reflection? How do people reflect on the data they collect in their daily lives? How can we measure technology's influence on reflection? These questions guided my PhD research.
As a starting point, we needed an overview of strategies to encourage reflection. Many prototypes have been developed to support users in reflecting on their health, productivity, and work-life balance, using techniques like data visualizations, questions, and leaderboards. Through a systematic literature review, we created an overview of all these strategies, resulting in a taxonomy with 11 strategies and 74 techniques for reflection.
Next, we need to understand how PI users reflect on their data in daily life. Interviews with fitness tracker users gave insight into how they reflect on their health and well-being. Based on these interviews, we developed a model describing how a system can create a temporary state of reflection. Lacking an instrument to evaluate systems, we developed a validated scale to measure whether technology supports reflection. This model and scale provide a theoretical foundation for future research into reflection in PI systems.
In the final part, we explored the impact of metrics on reflection. The earlier studies showed that reflection is promoted only if the data collected is relevant to the user. To investigate this further, we conducted two additional studies. The first examined how a metric's abstraction level affects reflection using a fictional "health score," leading to three design guidelines: metrics should form a coherent story, help users connect data, and explain how they're measured. The second study explored how data physicalization (making data tangible) can promote reflection on complex metrics like blood pressure. We found that tangible data can encourage reflection but requires guidance.
In conclusion, this thesis contributes to the understanding of technology-supported reflection through a structured literature review, a taxonomy of strategies, a user reflection model, a validated scale, guidelines for designing reflective metrics, and an exploration of the use of tangible data to promote reflection. As such, this thesis lays the foundation for developing technology that supports data-driven reflection
Hybrid Cognitive-Affective Strategies for AI Safety
No full textThe steadily increasing capabilities in AI systems can have tremendous beneficial
impacts on society. However, it is important to simultaneously tackle possible risks
that these developments are accompanied by. Therefore, the relatively young field of
AI safety has gained international relevance. In parallel, popular media were
commenting on whether society should ascribe motifs such as fear or enthusiasm to
AI. However, in order to assess the landscape of AI risks and opportunities, it is
instead first and foremost of relevance not to be afraid, not to be enthusiastic, but to
understand as similarly suggested by Spinoza in the 17th century. In this vein, in this
thesis, a transdisciplinary examination is performed to understand how to address
possible instantiations of AI risks with the aid of scientifically grounded hybrid
cognitive-affective strategies. The identified strategies are “hybrid" due to the fact that AI systems cannot be analyzed in isolation and the nature of human entities as well as the properties of human-machine interactions have to be taken into account within a socio-technological framework and not only addressing unintentional failures but also intentional malice. Consequently, the attribute “cognitive-affective" refers to the inherently affective nature of human cognition.
We consider two disjunct sets of systems: Type I and Type II systems. Type II
systems are systems that are able to consciously create and understand explanatory
knowledge. Conversely, Type I systems are all systems that do not exhibit this
ability. All current AIs are of Type I. However, even if Type II AI is non-existent nowadays, its implementation is not physically impossible. Overall, we identify the following non-exhaustive set of 10 tailored hybrid cognitive-affective strategical clusters for AI safety 1) international (meta-)goals, 2) transdisciplinary Type I/II AI safety and related education, 3) socio-technological feedback-loop, 4) integration of affective, dyadic and social information, 5) security measures and ethical adversarial examples research, 6) virtual reality frameworks, 7) orthogonality-based disentanglement of responsibilities, 8) augmented utilitarianism and ethical goal functions, 9) AI self-awareness and 10) artificial
creativity augmentation research.
In the thesis, we also introduce the so-called AI safety paradox stating, figuratively
speaking, that value alignment and control represent conjugate requirements. In
theory, with a Type II AI, a mutual value alignment might be achievable via a co-
construction of novel values, however, at the cost of its predictability. Conversely, it
is possible to build Type I AI systems that are controllable and predictable, but they
would not exhibit a sufficient understanding of human morality. Nevertheless, AI safety can be addressed by a cybersecurity oriented and risk-centered approach reformulating AI safety as a discipline which proactively addresses AI risks and reactively responds to occurring instantiations of AI risks. In a nutshell, future AI safety requires transdisciplinarily conceived and scientifically grounded dynamics combining proactive error-prediction and reactive error-correction within a socio-technological feedback-loop together with the cognizance that it is first of relevance not to be afraid, not to be enthusiastic, but to understand – that the price of security is eternal creativity
User navigation and guidance
No full textA new approach for user navigation and guidance is described in which initiative and topic selection by an interactive instruction system can be combined with initiative and topic selection by a student. The topic selection of the system is based on foreknowledge, goals and the capabilities of the individual student. In an experiment, we found that topic selection by the system had an advantage for students who were unable to monitor their own learning process
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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