Refine
Departments, institutes and facilities
Document Type
- Conference Object (52)
- Article (20)
- Part of a Book (8)
- Working Paper (2)
- Doctoral Thesis (1)
Year of publication
Keywords
Die nutzerInnenfreundliche Formulierung von Zwecken der Datenverarbeitung von Sprachassistenten
(2020)
2019 wurde bekannt, dass mehrere Anbieter von Sprachassistenten Sprachaufnahmen ihrer NutzerInnen systematisch ausgewertet haben. Da in den Datenschutzhinweisen angegeben war, dass Daten auch zur Verbesserung des Dienstes genutzt würden, war diese Nutzung legal. Für die NutzerInnen stellte diese Auswertung jedoch einen deutlichen Bruch mit ihren Privatheitsvorstellungen dar. Das Zweckbindungsprinzip der DSGVO mit seiner Komponente der Zweckspezifizierung fordert neben Flexibilität für den Verarbeiter auch Transparenz für den Verbraucher. Vor dem Hintergrund dieses Interessenkonflikts stellt sich für die HCI die Frage, wie Verarbeitungszwecke von Sprachassistenten gestaltet sein sollten, um beide Anforderungen zu erfüllen. Für die Erhebung einer Nutzerperspektive analysiert diese Studie zunächst Zweckangaben in den Datenschutzhinweisen der dominierenden Sprachassistenten. Darauf aufbauend präsentieren wir Ergebnisse von Fokusgruppen, die sich mit der wahrgenommenen Verarbeitung von Daten von Sprachassistenten aus Nutzersicht befassen. Es zeigt sich, dass bestehende Zweckformulierungen für VerbraucherInnen kaum Transparenz über Folgen der Datenverarbeitung bieten und keine einschränkende Wirkung im Hinblick auf legale Datennutzung erzielen. Unsere Ergebnisse über von Nutzern wahrgenommene Risiken erlauben dabei Rückschlüsse auf die anwenderfreundliche Gestaltung von Verarbeitungszwecken im Sinne einer Design-Ressource.
3D Printers as Sociable Technologies: Taking Appropriation Infrastructures to the Internet of Things
(2017)
Beyond HCI and CSCW: Challenges and Useful Practices Towards a Human-Centred Vision of AI and IA
(2019)
Focus on what matters: improved feature selection techniques for personal thermal comfort modelling
(2022)
Occupants' personal thermal comfort (PTC) is indispensable for their well-being, physical and mental health, and work efficiency. Predicting PTC preferences in a smart home can be a prerequisite to adjusting the indoor temperature for providing a comfortable environment. In this research, we focus on identifying relevant features for predicting PTC preferences. We propose a machine learning-based predictive framework by employing supervised feature selection techniques. We apply two feature selection techniques to select the optimal sets of features to improve the thermal preference prediction performance. The experimental results on a public PTC dataset demonstrated the efficiency of the feature selection techniques that we have applied. In turn, our PTC prediction framework with feature selection techniques achieved state-of-the-art performance in terms of accuracy, Cohen's kappa, and area under the curve (AUC), outperforming conventional methods.
Voice assistants (VA) collect data about users’ daily life including interactions with other connected devices, musical preferences, and unintended interactions. While users appreciate the convenience of VAs, their understanding and expectations of data collection by vendors are often vague and incomplete. By making the collected data explorable for consumers, our research-through-design approach seeks to unveil design resources for fostering data literacy and help users in making better informed decisions regarding their use of VAs. In this paper, we present the design of an interactive prototype that visualizes the conversations with VAs on a timeline and provides end users with basic means to engage with data, for instance allowing for filtering and categorization. Based on an evaluation with eleven households, our paper provides insights on how users reflect upon their data trails and presents design guidelines for supporting data literacy of consumers in the context of VAs.
Appropriating Digital Fabrication Technologies — A comparative study of two 3D Printing Communities
(2015)
Digital fabrication technologies have a great potential for empowering consumers to produce their own creations. However, despite the growing availability of digital fabrication technologies in shared machine shops such as FabLabs or University Labs, they are often perceived as difficult to use, especially by users with limited technological aptitude. Hence, it is not yet clear if the potentials of the technology can be made accessible to a broader public, or if they will remain limited to some form of “maker elite”. In this paper, we study the appropriation of digital fabrication on the example of the use of 3D printers in two different communities. In doing so, we analyze how users conceptualize their use of the 3D printers, what kind of contextual understanding is necessary to work with the machines, and how users document and share their knowledge. Based on our empirical findings, we identify the potentials that the machines offer to the communities, and what kind of challenges have to be overcome in their appropriation of the technology.
Software offshoring has been established as an important business strategy over the last decade. While research on such forms of Global Software Development (GSD) has mainly focused on the situation of large enterprises, small enterprises are increasingly engaging in offshoring, too. Representing the biggest share of the German software industry, small companies are known to be important innovators and market pioneers. They often regard their flexibility and customer-orientation as core competitive advantages. Unlike large corporations, their small size allows them to adopt software development approaches that are characterized by a high agility and flat hierarchies. At the same time, their distinct strategies make it unlikely that they can simply adopt management strategies that were developed for larger companies.
Flexible development approaches like the ones preferred by small corporations have proven to be problematic in the context of offshoring, as their strong dependency on constant communication is strongly affected by the various barriers of international cooperation between companies. Cooperating closely over companies’ borders in different time zones and in culturally diverse teams poses complex obstacles for flexible management approaches. It is still a matter of discussion in fields like Software Engineering and Computer Supported Cooperative Work how these obstacles can be tackled and how they affect companies in the long term. Hence, it is agreed that we need a more detailed understanding of distributed software development practices in order to come to feasible technological and organizational solutions.
This dissertation presents results from two ethnographically-informed case studies of software offshoring in small German enterprises. By adopting Anselm Strauss’ concept of articulation work, we want to deepen the understanding of managing distributed software development in flexible, customer-oriented organizations. In doing so, we show how practices of coordinating inter-organizational software development are closely related to aspects of organizational learning in small enterprises. By means of interviews with developers and project managers from both parties of the cooperation, we do not only take into account the multiple perspectives of the cooperation, but also include the socio-cultural background of international software development projects into our analysis.