Institut für Verbraucherinformatik (IVI)
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This research paper investigates the temporal and mental workload as well as work satisfaction regarding bureaucratic, administrative household labor, with a focus on socio-demographic differences. The study utilizes a paid online survey with 617 socio-demographically distributed participants. The results show significant differences in the temporal workload of different chore categories and in the quality of work, whereby satisfaction and mental workload are examined. In addition, the influences of gender, age, and education are analyzed, revealing differences in temporal and mental workload as well as work satisfaction. Our findings confirm prevailing literature showing that women have lower work satisfaction and a higher workload. In addition, we also discovered that younger people and groups of people with higher incomes have a higher level of satisfaction and a higher workload. In our study, a perceived high mental workload does not necessarily go hand in hand with a low level of satisfaction. This study contributes to the understanding of the bureaucratic burden on adults in their households and the variety of activities to manage private life.
Enlarged Education – Exploring the Use of Generative AI to Support Lecturing in Higher Education
(2024)
The rapid progress in sensor technology has empowered smart home systems to efficiently monitor and control household appliances. AI-enabled smart home systems can forecast household future energy demand so that the occupants can revise their energy consumption plan and be aware of optimal energy consumption practices. However, deep learning (DL)-based demand forecasting models are complex and decisions from such black-box models are often considered opaque. Recently, eXplainable Artificial Intelligence (XAI) has garnered substantial attention in explaining decisions of complex DL models. The primary objective is to enhance the acceptance, trust, and transparency of AI models by offering explanations about provided decisions. We propose ForecastExplainer, an explainable deep energy demand forecasting framework that leverages Deep Learning Important Features (DeepLIFT) to approximate Shapley values to map the contribution of different appliances and features with time. The generated explanations can shed light to explain the prediction highlighting the impact of energy consumption attributes corresponding to time, such as responsible appliances, consumption by household areas and activities, and seasonal effects. Experiments on household datasets demonstrated the effectiveness of our method in accurate forecasting. We designed a new metric to evaluate the effectiveness of the generated explanations and the experiment results indicate the comprehensibility of the explanations. These insights might empower users to optimize energy consumption practices, fostering AI adoption in smart applications.
As voice assistants (VAs) become more advanced leveraging Large Language Models (LLMs) and natural language processing, their potential for accountable behavior expands. Yet, the long-term situational effectiveness of VAs’ accounts when errors occur remains unclear. In our 19-month exploratory study with 19 households, we investigated the impact of an Alexa feature that allows users to inquire about the reasons behind its actions. Our findings indicate that Alexa's accounts are often single, decontextualized responses that led to users’ alternative repair strategies over the long term, such as turning off the device, rather than initiating a dialogue about what went wrong. Through role-playing workshops, we demonstrate that VA interactions should facilitate explanatory dialogues as dynamic exchanges that consider a range of speech acts, recognizing users’ emotional states and the context of interaction. We conclude by discussing the implications of our findings for the design of accountable VAs.
Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments
(2024)
The air quality in many German cities does not comply with EU-wide standards. Vehicle emissions, in particular, have been identified as an important cause of air pollution. As a result, driving bans for diesel vehicles with critical pollutant groups have been imposed by courts in many places in recent history. Against the backdrop of the growth of major German cities over the last few years, the question has become whether and how a change in the modal split in favor of more environmentally and climate-friendly public transport sector can be achieved. The Federal City of Bonn is one of five model cities that is testing measures to reduce traffic-related nitrogen dioxide emissions through a Climate Ticket as a mobility flat rate for one year for 365 €, which is part of the two-year "Lead City" project funded by the federal government. A quantitative survey (n = 1,315) of Climate Ticket users as well as the logistic regression carried out confirm that a change in individual mobility behavior in favor of public transport is possible by subsidizing the ticket price. The results show that individual traffic could be saved on the city's main congestion axes. In order to achieve a sustainable improvement in air quality, such a Climate Ticket must be granted on a permanent basis, with a well-designed mobility offer and must be generous in terms of the group of authorized persons and the area of validity.
Verbraucherinformatik
(2024)
In einer Zeit, in der digitale Technologien nahezu jeden Aspekt unseres Lebens durchdringen, ist es unerlässlich, die tieferen Zusammenhänge des digitalen Konsums zu verstehen. Erstmalig bietet dieses open access-Lehrbuch einen Wegweiser durch die vielfältigen Facetten der Digitalisierung des Konsums. Dabei verbindet es die Disziplinen der angewandten Informatik und Verbraucherwissenschaften.
Die Leserinnen und Leser erhalten Einblick in die digitale Konsumlandschaft, ausgehend von der historischen Entwicklung des (digitalen) Konsums. Dazu vermittelt das Lehrbuch zentrale Grundbegriffe und Themen der Verbraucherinformatik und stellt verschiedene Konsumtheorien aus den Disziplinen Wirtschaftswissenschaften, Psychologie und Sozialwissenschaften vor. Praxisnahe Beispiele aus der Digitalisierung bieten Einsichten in unterschiedliche Perspektiven, während vertiefende Textboxen und Selbstreflexionsfragen das Verständnis fördern.
Inhaltlich decken die Autorinnen und Autoren Themen von Datenschutz bis zur Sharing Economy ab und geben insbesondere auch praktische Ansätze für Themen wie Verbraucherschutz und Nachhaltigkeit mit auf den Weg. Die Anwendungs- und Querschnittsthemen der Verbraucherinformatik reichen von der Digitalisierung der Haushalte und Märkte über Fragen des digitalen Verbraucherschutzes bis hin zu zentralen gesellschaftlichen Fragestellungen rund um die Themen Fairness, Verantwortung und Nachhaltigkeit bei der Gestaltung von digitalen Technologien.
Das Buch bietet einen umfassenden Überblick, der sowohl für Studierende der Wirtschafts- und Sozialwissenschaften als auch der angewandten Informatik von bedeutendem Wert ist.
Einordnung und Hintergrund
(2024)
Digitale Produkte und Dienstleistungen sind integraler Bestandteil des Alltags. Mobile Geräte sind in jedem Bereich präsent, vom Finanzmanagement bis zur Gesundheitsversorgung. Die Art des Konsums hat sich mit der Digitalisierung des Alltags der Verbraucher:innen grundlegend gewandelt, was neben Fragen nach Geschäftsmodellen auch solche nach Verbraucherschutz und Nachhaltigkeit aufwirft. Die Verbraucherinformatik untersucht diese Entwicklungen und ihre Auswirkungen auf Gesellschaft und Individuen. Dieses Kapitel gibt eine Einführung in die Disziplin und skizziert die Entwicklung des digitalen Konsums sowie die damit verbundenen Veränderungen für die Verbraucher:innen von der Verbreitung der ersten Heimcomputer bis heute. Zudem stellt es zentrale Grundbegriffe vor und gibt einen Überblick über das didaktische Konzept sowie die Inhalte der weiteren Kapitel des Lehrbuchs.
In diesem Kapitel werden wichtige theoretische Konsumtheorien und ihr Bezug zur Verbraucherinformatik besprochen. Hierzu gehören Markttheorien, die die Verbraucher:in als rationalen Marktakteur verstehen, und psychologische Ansätze, die das individuelle Konsumverhalten auf Basis von Bedürfnissen, Kognition und Emotionen zu erklären versuchen. Darüber hinaus werden gesellschafts- und kulturwissenschaftliche Ansätze vorgestellt, welche die gesellschaftliche Prägung und symbolische Bedeutung des Konsums betonen. Ein Fokus liegt dabei auf praxistheoretischen Ansätzen, die vor allem die materielle Ebene, die performative Ausgestaltung und die Routinehaftigkeit von Konsum in den Blick nehmen. Ziel des Kapitels ist es, die verschiedenen Ansätze mit ihren jeweiligen Stärken und Schwächen vorzustellen und Leser:innen Orientierungswissen mit an die Hand zu geben, welche theoretische Linse für welche Fragestellung geeignet ist, da die Wahl eines bestimmten Ansatzes von der konkreten Forschungsfrage abhängt.
Digitaler Haushalt und Markt
(2024)
In diesem Kapitel werden die Perspektive des Privathaushalts und die Perspektive des Marktes erörtert. Ein:e Verbraucher:in, welche:r innerhalb eines Haushalts zur Bedürfnisbefriedigung wirtschaftet, erledigt dabei Hausarbeit durch Kochen, Putzen, Waschen, tätigt den Abschluss von Verträgen, die Pflege von Einkaufslisten oder eine Finanzplanung. Dabei haben Verbraucher:innen unterschiedliche Praktiken und Bewältigungsstrategien entwickelt, die es in der Verbraucherinformatik zu analysieren gilt. Physische Hausarbeit wird bereits im Smart-Home-Kontext durch intelligente Maschinen (oft Roboter genannt) ausgelagert. Kognitive Hausarbeit im alltäglichen Handeln (bspw. bei Verträgen) kann und wird in Zukunft durch Software wie auch Intermediäre unterstützt werden. Digitale Märkte stellen eine häufige Form der Intermediation dar, die Besonderheiten und Effekten unterliegt, welche das Verbraucherverhalten beeinflussen können. So gewinnen zum Beispiel einige wenige Anbieter mithilfe von Netzwerkeffekten, Skaleneffekten und Lock-in-Effekten zunehmend an Marktdominanz und verdrängen kleinere Anbieter, was zu Quasimonopolen führen kann. Digitale Märkte zeichnen sich zudem durch ihre Vertrauensfunktion in der Internet-Ökonomie aus.
Digitaler Verbraucherschutz
(2024)
Verbraucher:innen hinterlassen Spuren in nahezu allen Bereichen und Lebensräumen. Besonders der stetig wachsende digitale Lebensraum ist voll von Informationen und Daten. Durch die Allgegenwärtigkeit datensammelnder Dienste und Geräte wie das Smartphone durchdringen diese immer tiefer auch die analogen Bereiche des Lebens. In diesem Kapitel geht es um Privatsphäre, Verbraucherdaten und die resultierende Cyberkriminalität. Es werden Wege aufgezeigt, wie Verbraucher:innen sensibilisiert und befähigt werden können, um sich selbst, ihre Privatsphäre und ihre Daten zu schützen. Außerdem geben wir einen Überblick, welche Arten von Cyberkriminalität es gibt und was darunter verstanden wird. Hierbei wird auf Verbraucherschutz, Privatsphäre und die verschiedenen Arten des Onlinebetrugs eingegangen. Wir bieten einen Einblick in die „digitale Resilienz“ von Verbraucher:innen und erfassen die verschiedenen Präventions- und Bewältigungsstrategien, die Opfer anwenden.
Digitale Verantwortung
(2024)
Die Verbreitung digitaler Systeme beeinflusst Entscheidungen, Gesetze, Verhalten und Werte in unserer Gesellschaft. Dies wirkt sich auf Konsumgewohnheiten, Marktbeziehungen, Machtverteilung, Privatsphäre und IT-Sicherheit aus. Damit einhergehende Veränderungen haben direkte Auswirkungen auf unser Leben, was im Bereich der Technikfolgenabschätzung bzw. der angewandten Informatik unter dem Stichwort ELSI diskutiert wird. Dieses Kapitel fokussiert auf entsprechende Fragestellungen bezüglich ethischer Auswirkungen. Insbesondere rückt Fairness im Kontext automatisierter Entscheidungen in den Fokus, da Verbraucher:innen diesen zunehmend ausgesetzt sind. Zudem wird im Rahmen der gestiegenen Besorgnis über ökologische Auswirkungen das Thema Nachhaltigkeit am Beispiel von „Sharing Economy“ und „Shared Mobility“ weiter vertieft.
Digitale Gestaltung
(2024)
Die digitale Gestaltung in der Verbraucherinformatik stellt den Menschen und seine Konsumpraktiken in den Gestaltungsmittelpunkt. Dieses Kapitel erläutert gängige Designansätze und -vorgehen in der Software-Artefaktgestaltung und diskutiert die nutzer:innenzentrierten Kriterien und Ziele, die durch die Gebrauchstauglichkeit (Usability) und das Nutzungserlebnis (UX) maßgeblich bestimmt werden. Mit Rückbezug auf soziale Praktiken werden diese und die gesellschaftliche Partizipation im Allgemeinen als Designmaterial vorgestellt. Abschließend werden in zwei Design Case Studies explorative Designansätze zur Gestaltung neuer Technologien exemplarisch erläutert und diskutiert.
Dark Patterns are deceptive designs that influence a user's interactions with an interface to benefit someone other than the user. Prior work has identified dark patterns in WIMP interfaces and ubicomp environments, but how dark patterns can manifest in Augmented and Virtual Reality (collectively XR) requires more attention. We therefore conducted ten co-design workshops with 20 experts in XR and deceptive design. Our participants co-designed 42 scenarios containing dark patterns, based on application archetypes presented in recent HCI/XR literature. In the co-designed scenarios, we identified ten novel dark patterns in addition to 39 existing ones, as well as ten examples in which specific characteristics associated with XR potentially amplified the effect dark patterns could have on users. Based on our findings and prior work, we present a classification of XR-specific properties that facilitate dark patterns: perception, spatiality, physical/virtual barriers, and XR device sensing. We also present the experts’ assessments of the likelihood and severity of the co-designed scenarios and highlight key aspects they considered for this evaluation, for example, technological feasibility, ease of upscaling and distributing malicious implementations, and the application's context of use. Finally, we discuss means to mitigate XR dark patterns and support regulatory bodies to reduce potential harms.
The digitization of financial activities in consumers' lives is increasing, and the digitalization of invoicing processes is expected to play a significant role, although this area is not well understood regarding the private sector. Human-Computer Interaction (HCI) and Computer Supported Cooperative Work (CSCW) research have a long history of analyzing the socio-material and temporal aspects of work practices that are relevant for the domestic domain. The socio-material structuring of invoicing work and the working styles of consumers must be considered when designing effective consumer support systems. In this ethnomethodologically-informed, design-oriented interview study, we followed 17 consumers in their daily practices of dealing with invoices to make the invisible administrative work involved in this process visible. We identified and described the meaningful artifacts that were used in a spatial-temporal process within various storage locations such as input, reminding, intermediate (for postponing cases) buffers, and archive systems. Furthermore, we identified three different working styles that consumers exhibited: direct completion, at the next opportunity, and postpone as far as possible. This study contributes to our understanding of household economics and domestic workplace studies in the tradition of CSCW and has implications for the design of electronic invoicing systems.
In recent years, eXtended Reality (XR) technology like Augmented Reality and Virtual Reality became both technically feasible as well as affordable which lead to a drastic demand of professionally designed and developed applications. However, this demand combined with a rapid pace of innovation revealed a lack of design tool support for professional interaction designers as well as a knowledge gap regarding their approaches and needs. To address this gap, this thesis engages with the work of professional XR interaction designers in a qualitative research into XR interaction design approach. Therefore, this thesis applies two complementary lenses stemming from scientific design and social practice theory discourses to observe, describe, analyze, and understand professional XR interaction designers' challenges and approaches with a focus on application prototyping.
Improved Thermal Comfort Model Leveraging Conditional Tabular GAN Focusing on Feature Selection
(2024)
The indoor thermal comfort in both homes and workplaces significantly influences the health and productivity of inhabitants. The heating system, controlled by Artificial Intelligence (AI), can automatically calibrate the indoor thermal condition by analyzing various physiological and environmental variables. To ensure a comfortable indoor environment, smart home systems can adjust parameters related to thermal comfort based on accurate predictions of inhabitants’ preferences. Modeling personal thermal comfort preferences poses two significant challenges: the inadequacy of data and its high dimensionality. An adequate amount of data is a prerequisite for training efficient machine learning (ML) models. Additionally, high-dimensional data tends to contain multiple irrelevant and noisy features, which might hinder ML models’ performance. To address these challenges, we propose a framework for predicting personal thermal comfort preferences, combining the conditional tabular generative adversarial network (CTGAN) with multiple feature selection techniques. We first address the data inadequacy challenge by applying CTGAN to generate synthetic data samples, incorporating challenges associated with multimodal distributions and categorical features. Then, multiple feature selection techniques are employed to identify the best possible sets of features. Experimental results based on a wide range of settings on a standard dataset demonstrated state-of-the-art performance in predicting personal thermal comfort preferences. The results also indicated that ML models trained on synthetic data achieved significantly better performance than models trained on real data. Overall, our method, combining CTGAN and feature selection techniques, outperformed existing known related work in thermal comfort prediction in terms of multiple evaluation metrics, including area under the curve (AUC), Cohen’s Kappa, and accuracy. Additionally, we presented a global, model-agnostic explanation of the thermal preference prediction system, providing an avenue for thermal comfort experiment designers to consciously select the data to be collected.
Vehicle emissions have been identified as a cause of air pollution and one of the major reasons why air quality in many large German cities such as Berlin, Bonn, Hamburg, Cologne or Munich does not meet EU-wide limits. As a result, in the recent past, judicial driving bans on diesel vehicles have been imposed in many places since those vehicles emit critical pollutant groups. For the increasing urban population, the challenge is whether and how a change of the modal split in favor of the more environmentally and climate-friendly public transport can be achieved.
This paper presents the case of the Federal City of Bonn, one of five model cities sponsored by the German federal government that are testing measures to reduce traffic-related pollutant emissions by expanding the range of public transport services on offer. We present the results of a quantitative survey (N = 14,296) performed in the Bonn/Rhein-Sieg area and the neighboring municipalities as well as the ensuing logistic regressions confirming that a change in individual mobility behavior in favor of public transport is possible through expanding services. Our results show that individual traffic could be reduced, especially on the city's main traffic axes. To sustainably improve air quality, such services must be made permanently available.
Smart heating systems are one of the core components of smart homes. A large portion of domestic energy consumption is derived from HVAC (heating, ventilation and air conditioning) systems, making them a relevant topic of the efforts to support an energy transition in private housing. For that reason, the technology has attracted attention both from the academic and the industry communities. User interfaces of smart heating systems have evolved from simple adjusting knobs to advanced data visualization interfaces, that allow for more advanced setting such as time tables and status information. With the advent of AI, we are interested in exploring how the interfaces will be evolving to build the connection between user needs and underlying AI system. Hence, this paper is targeted to provide early design implications towards an AI-based user interface for smart heating systems.
AI systems pose unknown challenges for designers, policymakers, and users which aggravates the assessment of potential harms and outcomes. Although understanding risks is a requirement for building trust in technology, users are often excluded from legal assessments and explanations of AI hazards. To address this issue we conducted three focus groups with 18 participants in total and discussed the European proposal for a legal framework for AI. Based on this, we aim to build a (conceptual) model that guides policymakers, designers, and researchers in understanding users’ risk perception of AI systems. In this paper, we provide selected examples based on our preliminary results. Moreover, we argue for the benefits of such a perspective.
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.
Konsument:innen scheint die Lust vergangen zu sein, individuellen Kleidungsstil auszudrücken, da der Onlinehandel zur Steigerung von Auswahlmöglichkeiten geführt hat. Dies mündet unter anderem in der Nutzung virtueller Stilberatungen. Diese Dienste dienen dazu, Kund:innen möglichst effizient, individuell und authentisch „zu machen“, und sind somit als paradoxaler Demokratisierungsprozess zu verstehen. Eine Erklärung für den Erfolg dieser Dienstleistungen soll mit Reckwitz’ Singularisierungsthese gestützt werden.
Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems
(2024)
Peer-to-peer sharing platforms become increasingly important in the platform economy. From an HCI-perspective, this development is of high interest, as those platforms mediate between different users. Such mediation entails dealing with various social issues, e.g., building trust between peers online without any physical presence. Peer ratings have proven to be an important mechanism in this regard. At the same time, scoring via car telematics become more common for risk assessment by car insurances. Since user ratings face crucial problems such as fake or biased ratings, we conducted a design case study to determine whether algorithm-based scoring has the potential to improve trust-building in P2P-carsharing. We started with 16 problem-centered interviews to examine how people understand algorithm-based scoring, we co-designed an app with scored profiles, and finally evaluated it with 12 participants. Our findings show that scoring systems can support trust-building in P2P-carsharing and give insights how they should be designed.
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.
Background
Consumers rely heavily on online user reviews when shopping online and cybercriminals produce fake reviews to manipulate consumer opinion. Much prior research focuses on the automated detection of these fake reviews, which are far from perfect. Therefore, consumers must be able to detect fake reviews on their own. In this study we survey the research examining how consumers detect fake reviews online.
Methods
We conducted a systematic literature review over the research on fake review detection from the consumer-perspective. We included academic literature giving new empirical data. We provide a narrative synthesis comparing the theories, methods and outcomes used across studies to identify how consumers detect fake reviews online.
Results
We found only 15 articles that met our inclusion criteria. We classify the most often used cues identified into five categories which were (1) review characteristics (2) textual characteristics (3) reviewer characteristics (4) seller characteristics and (5) characteristics of the platform where the review is displayed.
Discussion
We find that theory is applied inconsistently across studies and that cues to deception are often identified in isolation without any unifying theoretical framework. Consequently, we discuss how such a theoretical framework could be developed.
Western consumption patterns are strongly associated with environmental pollution and climate change, which challenges us with transforming our society and consumption towards a sustainable future. This thesis takes up this challenge and aims to contribute to this debate at the intersection of ICT artifacts and social practices through the examples of food and mobility consumption. The social practice lens is employed as an alternative to the predominant persuasive or motivational lens of design in the respective consumption domains. Against this background, this thesis first presents three research papers that contribute to a broader understanding of dynamic practices and their transformation towards a sustainable stable state. The following research takes up these sections' empirical results that more intensely focus on the appropriation of materials and infrastructures utilizing Recommender Systems. Given this approach, this thesis contributes to three fields - practice-based Computing, Recommender Systems, and Consumer Informatics.
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.
Trust your guts: fostering embodied knowledge and sustainable practices through voice interaction
(2023)
Despite various attempts to prevent food waste and motivate conscious food handling, household members find it difficult to correctly assess the edibility of food. With the rise of ambient voice assistants, we did a design case study to support households’ in situ decision-making process in collaboration with our voice agent prototype, Fischer Fritz. Therefore, we conducted 15 contextual inquiries to understand food practices at home. Furthermore, we interviewed six fish experts to inform the design of our voice agent on how to guide consumers and teach food literacy. Finally, we created a prototype and discussed with 15 consumers its impact and capability to convey embodied knowledge to the human that is engaged as sensor. Our design research goes beyond current Human-Food Interaction automation approaches by emphasizing the human-food relationship in technology design and demonstrating future complementary human-agent collaboration with the aim to increase humans’ competence to sense, think, and act.
Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those models are often complex and not easily understandable to the company’s stakeholders, i.e. the people having to follow up on recommendations or try to understand automated decisions of a system. This opaqueness and black-box nature might hinder adoption, as users struggle to make sense and trust the predictions of AI models. Recent research on eXplainable Artificial Intelligence (XAI) focused mainly on explaining the models to AI experts with the purpose of debugging and improving the performance of the models. In this article, we explore how such systems could be made explainable to the stakeholders. For doing so, we propose a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction in inventory management. Backorders are orders that customers place for products that are currently not in stock. The company now takes the risk to produce or acquire the backordered products while in the meantime, customers can cancel their orders if that takes too long, leaving the company with unsold items in their inventory. Hence, for their strategic inventory management, companies need to make decisions based on assumptions. Our argument is that these tasks can be improved by offering explanations for AI recommendations. Hence, our research investigates how such explanations could be provided, employing Shapley additive explanations to explain the overall models’ priority in decision-making. Besides that, we introduce locally interpretable surrogate models that can explain any individual prediction of a model. The experimental results demonstrate effectiveness in predicting backorders in terms of standard evaluation metrics and outperform known related works with AUC 0.9489. Our approach demonstrates how current limitations of predictive technologies can be addressed in the business domain.
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.
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.
While the recent discussion on Art. 25 GDPR often considers the approach of data protection by design as an innovative idea, the notion of making data protection law more effective through requiring the data controller to implement the legal norms into the processing design is almost as old as the data protection debate. However, there is another, more recent shift in establishing the data protection by design approach through law, which is not yet understood to its fullest extent in the debate. Art. 25 GDPR requires the controller to not only implement the legal norms into the processing design but to do so in an effective manner. By explicitly declaring the effectiveness of the protection measures to be the legally required result, the legislator inevitably raises the question of which methods can be used to test and assure such efficacy. In our opinion, extending the legal compatibility assessment to the real effects of the required measures opens this approach to interdisciplinary methodologies. In this paper, we first summarise the current state of research on the methodology established in Art. 25 sect. 1 GDPR, and pinpoint some of the challenges of incorporating interdisciplinary research methodologies. On this premise, we present an empirical research methodology and first findings which offer one approach to answering the question on how to specify processing purposes effectively. Lastly, we discuss the implications of these findings for the legal interpretation of Art. 25 GDPR and related provisions, especially with respect to a more effective implementation of transparency and consent, and provide an outlook on possible next research steps.
The corporate landscape is experiencing an increasing change in business models due to digitization. An increasing availability of data along the business processes enhance the opportunities for process automation. Technologies such as Robotic Process Automation (RPA) are widely used for business process optimization, but as a side effect an increase in stand-alone solutions and a lack of holistic approaches can be observed. Intelligent Process Automation (IPA) is said to support more complex processes and enable automated decision-making, but due to the lack of connectors makes the implementation difficult. RPA marketplaces can be a bridging technology to help companies implement Intelligent Process Automation. This paper explores the drivers and challenges for the adoption of RPA marketplaces to realize IPA. For this purpose, we conducted ten expert interviews with decision makers and IT staff from the process automation sector.
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?
Regions and their innovation ecosystems have increasingly become of interest to CSCW research as the context in which work, research and design takes place. Our study adds to this growing discourse, by providing preliminary data and reflections from an ongoing attempt to intervene and support a regional innovation ecosystem. We report on the benefits and shortcomings of a practice-oriented approach in such regional projects and highlight the importance of relations and the notion of spillover. Lastly, we discuss methodological and pragmatic hurdles that CSCW research needs to overcome in order to support regional innovation ecosystems successfully.
