This exploratory study investigates the rationale behind categorizing algorithmic controls, or algorithmic affordances, in the graphical user interfaces (GUIs) of recommender systems. Seven professionals from industry and academia took part in an open card sorting activity to analyze 45 cards with examples of algorithmic affordances in recommender systems’ GUIs. Their objective was to identify potential design patterns including features on which to base these patterns. Analyzing the group discussions revealed distinct thought processes and defining factors for design patterns that were shared by academic and industry partners. While the discussions were promising, they also demonstrated a varying degree of alignment between industry and academia when it came to labelling the identified categories. Since this workshop is part of the preparation for creating a design pattern library of algorithmic affordances, and since the library aims to be useful for both industry and research partners, further research into design patterns of algorithmic affordances, particularly in terms of labelling and description, is required in order to establish categories that resonate with all relevant parties
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Recommenders play a significant role in our daily lives, making decisions for users on a regular basis. Their widespread adoption necessitates a thorough examination of how users interact with recommenders and the algorithms that drive them. An important form of interaction in these systems are algorithmic affordances: means that provide users with perceptible control over the algorithm by, for instance, providing context (‘find a movie for this profile’), weighing criteria (‘most important is the main actor’), or evaluating results (‘loved this movie’). The assumption is that these algorithmic affordances impact interaction qualities such as transparency, trust, autonomy, and serendipity, and as a result, they impact the user experience. Currently, the precise nature of the relation between algorithmic affordances, their specific implementations in the interface, interaction qualities, and user experience remains unclear. Subjects that will be discussed during the workshop, therefore, include but are not limited to the impact of algorithmic affordances and their implementations on interaction qualities, balances between cognitive overload and transparency in recommender interfaces containing algorithmic affordances; and reasons why research into these types of interfaces sometimes fails to cross the research-practice gap and are not landing in the design practice. As a potential solution the workshop committee proposes a library of examples of algorithmic affordances design patterns and their implementations in recommender interfaces enriched with academic research concerning their impact. The final part of the workshop will be dedicated to formulating guiding principles for such a library.
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Algorithmic affordances are defined as user interaction mechanisms that allow users tangible control over AI algorithms, such as recommender systems. Designing such algorithmic affordances, including assessing their impact, is not straightforward and practitioners state that they lack resources to design adequately for interfaces of AI systems. This could be amended by creating a comprehensive pattern library of algorithmic affordances. This library should provide easy access to patterns, supported by live examples and research on their experiential impact and limitations of use. The Algorithmic Affordances in Recommender Interfaces workshop aimed to address key challenges related to building such a pattern library, including pattern identification features, a framework for systematic impact evaluation, and understanding the interaction between algorithmic affordances and their context of use, especially in education or with users with a low algorithmic literacy. Preliminary solutions were proposed for these challenges.
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Intelligent environments can offer support to people with early-stage dementia, who often experience problems with maintaining their circadian rhythm. The focus of this work is developing a prototype of an Intelligent Environment for assisting these people with their daily rhythm while living independently at home. Following the four phases of the Empathic Design Framework (Explore, Translate, Process, and Validate), the needs of people with dementia and their caregivers were incorporated into the design. In the exploration phase, a need assessment took place using focus groups (N=12), observations (N=10), and expert interviews (N=27). Then, to determine the requirements for a prototype of an intelligent environment, the second phase, Translate, used three co-creation sessions with different stakeholder groups. In these sessions, Mind Maps (N=55) and Idea Generation Cards (N=35) were used. These resulted in a set of 10 requirements on the following topics: context-awareness, pattern recognition, adaptation, support, personalization, autonomy, modularity, dementia proof interaction, costs, data, and privacy. Finally, in the third phase, the requirements were applied to a real-life prototype by a multidisciplinary design team of researchers, (E-Health) tech companies, designers, software engineers with representatives of eight organizations. The prototype serves as a basis for further development of Intelligent Environments to enable people with dementia to live longer independently at home.
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Conference proceedings International Symposium on Intelligent Manufacturing Environments
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The user’s experience with a recommender system is significantly shaped by the dynamics of user-algorithm interactions. These interactions are often evaluated using interaction qualities, such as controllability, trust, and autonomy, to gauge their impact. As part of our effort to systematically categorize these evaluations, we explored the suitability of the interaction qualities framework as proposed by Lenz, Dieffenbach and Hassenzahl. During this examination, we uncovered four challenges within the framework itself, and an additional external challenge. In studies examining the interaction between user control options and interaction qualities, interdependencies between concepts, inconsistent terminology, and the entity perspective (is it a user’s trust or a system’s trustworthiness) often hinder a systematic inventory of the findings. Additionally, our discussion underscored the crucial role of the decision context in evaluating the relation of algorithmic affordances and interaction qualities. We propose dimensions of decision contexts (such as ‘reversibility of the decision’, or ‘time pressure’). They could aid in establishing a systematic three-way relationship between context attributes, attributes of user control mechanisms, and experiential goals, and as such they warrant further research. In sum, while the interaction qualities framework serves as a foundational structure for organizing research on evaluating the impact of algorithmic affordances, challenges related to interdependencies and context-specific influences remain. These challenges necessitate further investigation and subsequent refinement and expansion of the framework.
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Het zwaartepunt van de ingenieursopleiding is aan het verschuiven. De Utrechtse ingenieur zal zijn werk en toegevoegde waarde steeds meer vinden op het terrein van ontwerpen. Aan het ontwerpproces zelf worden steeds zwaardere eisen gesteld. Constructie en productie vinden in toenemende mate elders in de wereld plaats. Gelet op deze outsourcing zal de ontwerper ook in staat moeten zijn het maakproces op afstand te besturen, zowel wat betreft kwaliteit en geld als qua tijd. Ontwerpen kan vanuit verschillende perspectieven beschouwd worden: vanuit de conceptuele fase, de realisatiefase (verdere aanpassingen) of de gebruiksfase (upgrading, bediening et cetera). Bij onderzoeksinstellingen als TNO, maar ook bij vooraanstaande bedrijven als OCE, Philips en ASML zien we dat steeds meer sprake is van een integrale ontwerpaanpak. Het tijdperk van massaproductie evolueert naar een tijdperk van maatwerk, waarin de behoeften van de gebruiker centraal staan. De interactie tussen de technologie en de gebruiker zal een steeds belangrijker plaats in gaan nemen, en juist op dit vlak zal de Utrechtse ingenieur zich onderscheiden.
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Post-partum hemorrhaging is a medical emergency that occurs during childbirth and, in extreme cases, can be life-threatening. It is the number one cause of maternal mortality worldwide. High-quality training of medical staff can contribute to early diagnosis and work towards preventing escalation towards more serious cases. Healthcare education uses manikin-based simulators to train obstetricians for various childbirth scenarios before training on real patients. However, these medical simulators lack certain key features portraying important symptoms and are incapable of communicating with the trainees. The authors present a digital embodiment agent that can improve the current state of the art by providing a specification of the requirements as well as an extensive design and development approach. This digital embodiment allows educators to respond and role-play as the patient in real time and can easily be integrated with existing training procedures. This research was performed in collaboration with medical experts, making a new contribution to medical training by bringing digital humans and the representation of affective interfaces to the field of healthcare.
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In discussions on smart grids, it is often stated that residential end-users will play a more active role in the management of the electric power system. Experience in practice on how to empower end-users for such a role is however limited. This paper presents a field study in the first phase of the PowerMatching City project in which twenty-two households were equipped with demand-response-enabled heating systems and white goods. Although end-users were satisfied with the degree of living comfort afforded by the smart energy system, the user interface did not provide sufficient control and energy feedback to support an active contribution to the balancing of supply and demand. The full potential of demand response was thus not realized. The second phase of the project builds on these findings by design, implementation and evaluation of an improved user interface in combination with two demand response propositions. © 2013 IEEE.
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B4B is a multi-year, multi-stakeholder project focused on developing methods to harness big data from smart meters, building management systems and the Internet of Things devices, to reduce energy consumption, increase comfort, respond flexibly to user behaviour and local energy supply and demand, and save on installation maintenance costs. This will be done through the development of faster and more efficient Machine Learning and Artificial Intelligence models and algorithms. The project is geared to existing utility buildings such as commercial and institutional buildings.
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