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When dialogues with voice assistants (VAs) fall apart, users often become confused or even frustrated. To address these issues and related privacy concerns, Amazon recently introduced a feature allowing Alexa users to inquire about why it behaved in a certain way. But how do users perceive this new feature? In this paper, we present preliminary results from research conducted as part of a three-year project involving 33 German households. This project utilized interviews, fieldwork, and co-design workshops to identify common unexpected behaviors of VAs, as well as users’ needs and expectations for explanations. Our findings show that, contrary to its intended purpose, the new feature actually exacerbates user confusion and frustration instead of clarifying Alexa's behavior. We argue that such voice interactions should be characterized as explanatory dialogs that account for VA’s unexpected behavior by providing interpretable information and prompting users to take action to improve their current and future interactions.
There has been a growing interest in taste research in the HCI and CSCW communities. However, the focus is more on stimulating the senses, while the socio-cultural aspects have received less attention. However, individual taste perception is mediated through social interaction and collective negotiation and is not only dependent on physical stimulation. Therefore, we study the digital mediation of taste by drawing on ethnographic research of four online wine tastings and one self-organized event. Hence, we investigated the materials, associated meanings, competences, procedures, and engagements that shaped the performative character of tasting practices. We illustrate how the tastings are built around the taste-making process and how online contexts differ in providing a more diverse and distributed environment. We then explore the implications of our findings for the further mediation of taste as a social and democratized phenomenon through online interaction.
Technological objects present themselves as necessary, only to become obsolete faster than ever before. This phenomenon has led to a population that experiences a plethora of technological objects and interfaces as they age, which become associated with certain stages of life and disappear thereafter. Noting the expanding body of literature within HCI about appropriation, our work pinpoints an area that needs more attention, “outdated technologies.” In other words, we assert that design practices can profit as much from imaginaries of the future as they can from reassessing artefacts from the past in a critical way. In a two-week fieldwork with 37 HCI students, we gathered an international collection of nostalgic devices from 14 different countries to investigate what memories people still have of older technologies and the ways in which these memories reveal normative and accidental use of technological objects. We found that participants primarily remembered older technologies with positive connotations and shared memories of how they had adapted and appropriated these technologies, rather than normative uses. We refer to this phenomenon as nostalgic reminiscence. In the future, we would like to develop this concept further by discussing how nostalgic reminiscence can be operationalized to stimulate speculative design in the present.
Taste is a complex phenomenon that depends on the individual experience and is a matter of collective negotiation and mediation. On the contrary, it is uncommon to include taste and its many facets in everyday design, particularly online shopping for fresh food products. To realize this unused potential, we conducted two Co-Design workshops. Based on the participants’ results in the workshops, we prototyped and evaluated a click-dummy smart-phone app to explore consumers’ needs for digital taste depiction. We found that emphasizing the natural qualities of food products, external reviews, and personalizing features lead to a reflection on the individual taste experience. The self-reflection through our design enables consumers to develop their taste competencies and thus strengthen their autonomy in decision-making. Ultimately, exploring taste as a social experience adds to a broader understanding of taste beyond a sensory phenomenon.
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.
For most people, using their body to authenticate their identity is an integral part of daily life. From our fingerprints to our facial features, our physical characteristics store the information that identifies us as "us." This biometric information is becoming increasingly vital to the way we access and use technology. As more and more platform operators struggle with traffic from malicious bots on their servers, the burden of proof is on users, only this time they have to prove their very humanity and there is no court or jury to judge, but an invisible algorithmic system. In this paper, we critique the invisibilization of artificial intelligence policing. We argue that this practice obfuscates the underlying process of biometric verification. As a result, the new "invisible" tests leave no room for the user to question whether the process of questioning is even fair or ethical. We challenge this thesis by offering a juxtaposition with the science fiction imagining of the Turing test in Blade Runner to reevaluate the ethical grounds for reverse Turing tests, and we urge the research community to pursue alternative routes of bot identification that are more transparent and responsive.
AI (artificial intelligence) systems are increasingly being used in all aspects of our lives, from mundane routines to sensitive decision-making and even creative tasks. Therefore, an appropriate level of trust is required so that users know when to rely on the system and when to override it. While research has looked extensively at fostering trust in human-AI interactions, the lack of standardized procedures for human-AI trust makes it difficult to interpret results and compare across studies. As a result, the fundamental understanding of trust between humans and AI remains fragmented. This workshop invites researchers to revisit existing approaches and work toward a standardized framework for studying AI trust to answer the open questions: (1) What does trust mean between humans and AI in different contexts? (2) How can we create and convey the calibrated level of trust in interactions with AI? And (3) How can we develop a standardized framework to address new challenges?
Aim of this study is to investigate the effects of user experience (UX) on shopping mall customers’ intention to use a social robot. Therefore, we used a Wizard of Oz approach that enabled data collection in situ. Quantitative data was obtained from a questionnaire completed by shopping mall customers who interacted with a social robot. Data was used in a regression analysis, where user experience factors served as predictors for robot use in retail. The regression model explains up to 23.2% of the variance in customers’ intention to use a social robot. In addition, we collected qualitative data on human-robot-interactions and used the data to complement the interpretation of statistical results. Our findings suggest that only hedonic qualities significantly contribute to the prediction of customers’ intention, that shopping mall customers are reluctant to grant pragmatic qualities to social robots, and that UX evaluation in HRI requires additional predictors.
New cars are increasingly "connected" by default. Since not having a car is not an option for many people, understanding the privacy implications of driving connected cars and using their data-based services is an even more pressing issue than for expendable consumer products. While risk-based approaches to privacy are well established in law, they have only begun to gain traction in HCI. These approaches are understood not only to increase acceptance but also to help consumers make choices that meet their needs. To the best of our knowledge, perceived risks in the context of connected cars have not been studied before. To address this gap, our study reports on the analysis of a survey with 18 open-ended questions distributed to 1,000 households in a medium-sized German city. Our findings provide qualitative insights into existing attitudes and use cases of connected car features and, most importantly, a list of perceived risks themselves. Taking the perspective of consumers, we argue that these can help inform consumers about data use in connected cars in a user-friendly way. Finally, we show how these risks fit into and extend existing risk taxonomies from other contexts with a stronger social perspective on risks of data use.
Critical consumerism is complex as ethical values are difficult to negotiate, appropriate products are hard to find, and product information is overwhelming. Although recommender systems offer solutions to reduce such complexity, current designs are not appropriate for niche practices and use non-personalized intransparent ethics. To support critical consumption, we conducted a design case study on a personalized food recommender system. Therefore, we first conducted an empirical pre-study with 24 consumers to understand value negotiations and current practices, co-designed the recommender system, and finally evaluated it in a real-world trial with ten consumers. Our findings show how recommender systems can support the negotiation of ethical values within the context of consumption practices, reduce the complexity of finding products and stores, and strengthen consumers. In addition to providing implications for the design to support critical consumption practices, we critically reflect on the scope of such recommender systems and its appropriation.
In the course of growing online retailing, recommendation systems have become established that derive recommendations from customers’ purchase histories. Recommending suitable food products can represent a lucrative added value for food retailers, but at the same time challenges them to make good predictions for repeated food purchases. Repeat purchase recommendations have been little explored in the literature. These predict when a product will be purchased again by a customer. This is especially important for food recommendations, since it is not the frequency of the same item in the shopping basket that is relevant for determining repeat purchase intervals, but rather their difference over time. In this paper, in addition to critically reflecting classical recommendation systems on the underlying repeat purchase context, two models for online product recommendations are derived from the literature, validated and discussed for the food context using real transaction data of a German stationary food retailer.
Diese Studie untersucht die Aneignung und Nutzung von Sprachassistenten wie Google Assistant oder Amazon Alexa in Privathaushalten. Unsere Forschung basiert auf zehn Tiefeninterviews mit Nutzern von Sprachassistenten sowie der Evaluation bestimmter Interaktionen in der Interaktionshistorie. Unsere Ergebnisse illustrieren, zu welchen Anlässen Sprachassistenten im heimischen Umfeld genutzt werden, welche Strategien sich die Nutzer in der Interaktion mit Sprachassistenten angeeignet haben, wie die Interaktion abläuft und welche Schwierigkeiten sich bei der Einrichtung und Nutzung des Sprachassistenten ergeben haben. Ein besonderer Fokus der Studie liegt auf Fehlinteraktionen, also Situationen, in denen die Interaktion scheitert oder zu scheitern droht. Unsere Studie zeigt, dass das Nutzungspotenzial der Assistenten häufig nicht ausgeschöpft wird, da die Interaktion in komplexeren Anwendungsfällen häufig misslingt. Die Nutzer verwenden daher den Sprachassistenten eher in einfachen Anwendungsfällen und neue Apps und Anwendungsfälle werden gar nicht erst ausprobiert. Eine Analyse der Aneignungsstrategien, beispielsweise durch eine selbst erstellte Liste mit Befehlen, liefert Erkenntnisse für die Gestaltung von Unterstützungswerkzeugen sowie die Weiterentwicklung und Optimierung von sprachbasierten Mensch-Maschine-Interfaces.
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.
In 1991 the researchers at the center for the Learning Sciences of Carnegie Mellon University were confronted with the confusing question of “where is AI” from the users, who were interacting with AI but did not realize it. Three decades of research and we are still facing the same issue with the AItechnology users. In the lack of users’ awareness and mutual understanding of AI-enabled systems between designers and users, informal theories of the users about how a system works (“Folk theories”) become inevitable but can lead to misconceptions and ineffective interactions. To shape appropriate mental models of AI-based systems, explainable AI has been suggested by AI practitioners. However, a profound understanding of the current users’ perception of AI is still missing. In this study, we introduce the term “Perceived AI” as “AI defined from the perspective of its users”. We then present our preliminary results from deep-interviews with 50 AItechnology users, which provide a framework for our future research approach towards a better understanding of PAI and users’ folk theories.
Trust is the lubricant of the sharing economy. This is true especially in peer-to-peer carsharing, in which one leaves a highly valuable good to a stranger in the hope of getting it back unscathed. Nowadays, ratings of other users are major mechanisms for establishing trust. To foster uptake of peer-to-peer carsharing, connected car technology opens new possibilities to support trust-building, e.g., by adding driving behavior statistics to users' profiles. However, collecting such data intrudes into rentees' privacy. To explore the tension between the need for trust and privacy demands, we conducted three focus group and eight individual interviews. Our results show that connected car technologies can increase trust for car owners and rentees not only before but also during and after rentals. The design of such systems must allow a differentiation between information in terms of type, the context, and the negotiability of information disclosure.
Usability und User Experience (UX) haben als Design-Aspekte in der Produktentwicklung zunehmend an Bedeutung gewonnen. Daher ist es sinnvoll, die organisationale Kompetenz zur Entwicklung von Produkten mit einer positiven UX zu stärken. Veränderungen in Organisationen sind jedoch mit großem Aufwand verbunden. Deshalb müssen Organisationen entscheiden, welche Aktivitäten zur Veränderung der eigenen Kompetenz durchgeführt werden sollen und welche nicht. Die bisherige Forschung hat sich weitgehend auf die Anwendbarkeit bestimmter Methoden im Projekt- und Produktkontext konzentriert. Um geeignete Aktivitäten zur Verbesserung der organisationalen UX-Kompetenz zu identifizieren, wurden 17 UX-Professionals befragt. Diese UX-Professionals haben mindestens zehn Jahre Erfahrung durch die Arbeit in mehreren Unternehmen und durch die Übernahme einer Führungsrolle im Bereich UX gesammelt. Aus diesen Interviews wurden 13 mögliche Maßnahmen zur Steigerung der UX-Kompetenz von Organisationen abgeleitet. Dazu gehören beispielsweise die Erhöhung der Kompetenz einzelner Mitarbeiter, das Teilen von UX-Erfolgsgeschichten oder das Ermöglichen von User Research.
Who do you trust: Peers or Technology? A conjoint analysis about computational reputation mechanisms
(2020)
Peer-to-peer sharing platforms are taking over an increasingly important role in the platform economy due to their sustainable business model. By sharing private goods and services, the challenge arises to build trust between peers online mostly without any kind of physical presence. Peer rating has been proven as an important mechanism. In this paper, we explore the concept called Trust Score, a computational rating mechanism adopted from car telematics, which can play a similar role in carsharing. For this purpose, we conducted a conjoint analysis where 77 car owners chose between fictitious user profiles. Our results show that in our experiment the telemetric-based score slightly outperforms the peer rating in the decision process, while the participants perceived the peer rating more helpful in retrospect. Further, we discuss potential benefits with regard to existing shortcomings of user rating, but also various concerns that should be considered in concepts like telemetric-based reputation mechanism that supplements existing trust factors such as user ratings.
Autonomous driving enables new mobility concepts such as shared-autonomous services. Although significant re-search has been done on passenger-car interaction, work on passenger interaction with robo-taxis is still rare. In this paper, we tackle the question of how passengers experience robo-taxis as a service in real-life settings to inform the interaction design. We conducted a Wizard of Oz study with an electric vehicle where the driver was hidden from the passenger to simulate the service experience of a robo-taxi. 10 participants had the opportunity to use the simulated shared-autonomous service in real-life situations for one week. By the week's end, 33 rides were completed and recorded on video. Also, we flanked the study conducting interviews before and after with all participants. The findings provided insights into four design themes that could inform the service design of robo-taxis along the different stages including hailing, pick-up, travel, and drop-off.
Eco-InfoVis at Work
(2020)
Bei genauer Betrachtung heutiger Sharing Plattformen wie AirBnB, Uber, Drivy oder Fairleihen fällt auf, dass diese eines gemein haben. Als Plattformökonomien basieren sie auf mindestens zwei Nutzergruppen, Anbietern und Nachfragern für Güter oder Dienstleistungen. Ein Problem solcher zweioder mehrseitigen Märkte ist jedoch häufig, dass der Wertezuwachs, der durch die Nutzer generiert wird, nicht gleichmäßig unter der Plattform und den aktiven Nutzern verteilt wird, sondern meist ausschließlich als Gewinn an die Plattformen geht. Mit der Blockchain-Technologie könnte dieses Problem gelöst werden, indem der Informations- und Wertetransfer sicher und dezentral organisiert wird und viele Funktionen traditioneller Intermediäre dadurch obsolet werden. Diese Arbeit bietet einen Überblick über Anwendungsfelder und das Grundkonzept der Sharing Economy. Wir zeigen auf, wie sich Geschäftsmodelle und Infrastrukturen in einer Blockchain abbilden lassen, welche Potentiale eine Blockchain-basierte Infrastruktur bietet, wann diese in der Sharing Economy sinnvoll sein kann und welche Probleme dadurch gelöst werden können.
Exploring Future Work - Co-Designing a Human-robot Collaboration Environment for Service Domains
(2020)
There has been increasing interest in the application of humanoid robots in service domains like retail or care homes in recent years. Here, most use cases focus on serving customer needs autonomously. Frequently, human intervention becomes necessary to support the robot in exceptional situations. However, direct intervention of service operators is often not possible and requires specialized personnel. In a co-design process with 13 service operators from a pharmacy, we designed a remote working environment for human-robot collaboration that enables first-time experiences and collaboration with robots. Five participants took part in an assessment study and reported on their experiences about the utility, usability and user experience. Results show that participants were able to control and train the robot through the remote control environment. We discuss implications of our results for future work in service domains and emphasize a shift of focus from full robot automatization to human-robot collaboration forms.
The design of self-driving cars is one of the most exciting and ambitious challenges of our days and every day, new research work is published. In order to give an orientation, this article will present an overview of various methods used to study the human side of autonomous driving. Simplifying roughly, you can distinguish between design science-oriented methods (such as Research through Design, Wizard of Oz or driving simulator ) and behavioral science methods (such as survey, interview, and observation). We show how these methods are adopted in the context of autonomous driving research and dis-cuss their strengths and weaknesses. Due to the complexity of the topic, we will show that mixed method approaches will be suitable to explore the impact of autonomous driving on different levels: the individual, the social interaction and society.
Bisherige Versuche der HCI-Community die Lebensmittelverschwendung oder den CO2-Fußabdruck zu reduzieren, basierten meist auf Persuasive Design Ansätzen. Diese werden jedoch kritisiert, die Alltagswelten und Konsumpraktiken für eine Langzeitwirkung nur unzureichend zu berücksichtigen. Das Problem aufgreifend, untersucht dieser Beitrag die Rolle (digitaler) Medien im Übergang zu einer veganen Ernährungspraktik. Hierfür wurden semi-strukturierte Interviews mit 9 VeganerInnen geführt und vor dem Hintergrund der Praxistheorie analysiert. Die Ergebnisse deuten dabei auf eine intensive Nutzung (digitaler) Medien, insbesondere in der frühen Phase der Änderung der Konsumpraktik. Statt Gamification oder Persuasive Design, zeigt sich Mediennutzung in Form von Irritation, Informationsbereitstellung zur Ausbildung eines vegan-spezifischen Konsumwissens sowie als Vermittler zwischen Gleichgesinnten.
Intelligentes Carsharing zur Förderung der urbanen Mobilität - Einfach Teilen : Schlussbericht
(2019)
Die Globalisierung führt zu immer komplexeren, für die Einzelnen kaum nachvollziehbaren Wertschöpfungsketten in der Lebensmittelindustrie. Zugleich eröffnet die Digitalisierung neue Möglichkeiten, Informationen entlang der Kette zu sammeln, und so mehr Transparenz und Vertrauen für den Verbraucherbeziehungsweise die Verbraucherin zu schaffen. Jedoch finden Verbraucherinformations-Apps wie fTRACE bisher nur eine geringe Verbreitung. Daher haben wir in einer qualitativen Studie mit 16 Teilnehmer/-innen Bedürfnisse und Nutzungshürden von Verbraucher/-innen im Zusammenhang mit Verbraucherinformations-Apps analysiert. Es zeigt sich, dass das Vertrauen in die Informationen, sowie der einfache Zugang dazu für Verbraucher/-innen zentral sind. Durch die gut sichtbare Bereitstellung der Informationen am Point-of-Sale, sowie der automatisierten Informationsversorgung z. B. mittels digitaler Kassenzettel in Kombination mit weiteren Verbraucher-Services kann die Bekanntheit und Akzeptanz von Rückverfolgbarkeitssystemen weiter gesteigert werden.
The alternative use of travel time is one of the widely discussed benefits of driverless cars. We therefore conducted 14 co-design sessions to examine how people manage their time, to determine how they perceive the value of time in driverless cars and to derive design implications. Our findings suggest that driverless mobility will affect both people’s use of travel time as well as their time management in general. The participants repeatedly stated the desire of completing tasks while traveling to save time for activities that are normally neglected in their everyday life. Using travel time efficiently requires using car space efficiently, too. We found out that the design concept of tiny houses could serve as common design pattern to deal with the limited space within cars and support diverse needs.