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Item User-driven prioritization of ethical principles for artificial intelligence systems(2024) Fernholz, Yannick; Ermakova, Tatiana; Fabian, B.; Buxmann, PeterDespite the progress of Artificial Intelligence (AI) and its contribution to the advancement of human society, the prioritization of ethical principles from the viewpoint of its users has not yet received much attention and empirical investigations. This is important to develop appropriate safeguards and increase the acceptance of AI-mediated technologies among all members of society. In this research, we collected, integrated, and prioritized ethical principles for AI systems with respect to their relevance in different real-life application scenarios. First, an overview of ethical principles for AI was systematically derived from various academic and non-academic sources. Our results clearly show that transparency, justice and fairness, non-maleficence, responsibility, and privacy are most frequently mentioned in this corpus of documents. Next, an empirical survey to systematically identify users’ priorities was designed and conducted in the context of selected scenarios: AI-mediated recruitment (human resources), predictive policing, autonomous vehicles, and hospital robots. We anticipate that the resulting ranking can serve as a valuable basis for formulating requirements for AI-mediated solutions and creating AI algorithms that prioritize user’s needs. Our target audience includes everyone who will be affected by AI systems, e.g., policy makers, algorithm developers, and system managers as our ranking clearly depicts user’s awareness regarding AI ethics.Item A review of technologies for collaborative online information seeking. On the contribution of collaborative argumentation(2021) Mayweg-Paus, Elisabeth; Zimmermann, Maria; Le, Nguyen-Thinh; Pinkwart, NielsIn everyday life, people seek, evaluate, and use online sources to underpin opinions and make decisions. While education must promote the skills people need to critically question the sourcing of online information, it is important, more generally, to understand how to successfully promote the acquisition of any skills related to seeking online information. This review outlines technologies that aim to support users when they collaboratively seek online information. Upon integrating psychological–pedagogical approaches on trust in and the sourcing of online information, argumentation, and computer-supported collaborative learning, we reviewed the literature (N= 95 journal articles) on technologies for collaborative online information seeking. The technologies we identified either addressed collaborative online information seeking as an exclusive process for searching for online information or, alternatively, addressed online information seeking within the context of a more complex learning process. Our review was driven by three main research questions: We aimed to understand whether and how the studies considered 1) the role of trust and critical questioning in the sourcing of online information, 2) the learning processes at play when information seekers engage in collaborative argumentation, and 3) what affordances are offered by technologies that support users’ collaborative seeking of online information. The reviewed articles that focused exclusively on technologies for seeking online information primarily addressed aspects of cooperation (e.g., task management), whereas articles that focused on technologies for integrating the processes of information seeking into the entire learning processes instead highlighted aspects of collaborative argumentation (e.g., exchange of multiple perspectives and critical questioning in argumentation). Seven of the articles referred to trust as an aspect of seekers’ sourcing strategies. We emphasize how researchers’, users’, and technology developers’ consideration of collaborative argumentation could expand the benefits of technological support for seeking online information.Item Pushing the Scalability of RDF Engines on IoT Edge Devices(2020) Le-Tuan, Anh; Hayes, Conor; Hauswirth, Manfred; Le-Phuoc, DanhSemantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be embedded throughout the architecture, in particular at edge nodes. RDF processing at the edge facilitates the deployment of semantic integration gateways closer to low-level devices. Our focus is on how to enable scalable and robust RDF engines that can operate on lightweight devices. In this paper, we have first carried out an empirical study of the scalability and behaviour of solutions for RDF data management on standard computing hardware that have been ported to run on lightweight devices at the network edge. The findings of our study shows that these RDF store solutions have several shortcomings on commodity ARM (Advanced RISC Machine) boards that are representative of IoT edge node hardware. Consequently, this has inspired us to introduce a lightweight RDF engine, which comprises an RDF storage and a SPARQL processor for lightweight edge devices, called RDF4Led. RDF4Led follows the RISC-style (Reduce Instruction Set Computer) design philosophy. The design constitutes a flash-aware storage structure, an indexing scheme, an alternative buffer management technique and a low-memory-footprint join algorithm that demonstrates improved scalability and robustness over competing solutions. With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than popular RDF engines such as Jena TDB (Tuple Database) and RDF4J, while consuming the same amount of memory. In particular, RDF4Led requires 10%–30% memory of its competitors to operate on datasets of up to 50 million triples. On memory-constrained ARM boards, it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB and RDF4J.Item Extending the framework of algorithmic regulation. The Uber case(2022) Eyert, Florian; Irgmaier, Florian; Ulbricht, LenaIn this article, we take forward recent initiatives to assess regulation based on contemporary computer technologies such as big data and artificial intelligence. In order to characterize current phenomena of regulation in the digital age, we build on Karen Yeung’s concept of “algorithmic regulation,” extending it by building bridges to the fields of quantification, classification, and evaluation research, as well as to science and technology studies. This allows us to develop a more fine-grained conceptual framework that analyzes the three components of algorithmic regulation as representation, direction, and intervention and proposes subdimensions for each. Based on a case study of the algorithmic regulation of Uber drivers, we show the usefulness of the framework for assessing regulation in the digital age and as a starting point for critique and alternative models of algorithmic regulation.Item Reinvigorating the Discourse on Human-Centered Artificial Intelligence in Educational Technologies(2021) Renz, André; Vladova, GerganaThe increasing relevance of artificial intelligence (AI) applications in various domains has led to high expectations of benefits, ranging from precision, efficiency, and optimization to the completion of routine or time-consuming tasks. Particularly in the field of education, AI applications promise immense innovation potential. A central focus in this field is on analyzing and evaluating learner characteristics to derive learning profiles and create individualized learning environments. The development and implementation of such AI-driven approaches are related to learners' data, and thus involves several privacies, ethics, and morality challenges. In this paper, we introduce the concept of human-centered AI, and consider how an AI system can be developed in line with human values without posing risks to humanity. Because the education market is in the early stages of incorporating AI into educational tools, we believe that this is the right time to raise awareness about the use of principles that foster human-centered values and help in building responsible, ethical, and value-oriented AI.Item Do open data impact citizens’ behavior? Assessing face mask panic buying behaviors during the Covid-19 pandemic(2022) Shibuya, Yuya; Lai, Chun-Ming; Hamm, Andrea; Takagi, Soichiro; Sekimoto, YoshihideData are essential for digital solutions and supporting citizens’ everyday behavior. Open data initiatives have expanded worldwide in the last decades, yet investigating the actual usage of open data and evaluating their impacts are insufficient. Thus, in this paper, we examine an exemplary use case of open data during the early stage of the Covid-19 pandemic and assess its impacts on citizens. Based on quasi-experimental methods, the study found that publishing local stores’ real-time face mask stock levels as open data may have influenced people’s purchase behaviors. Results indicate a reduced panic buying behavior as a consequence of the openly accessible information in the form of an online mask map. Furthermore, the results also suggested that such open-data-based countermeasures did not equally impact every citizen and rather varied among socioeconomic conditions, in particular the education level.Item Algorithmic regulation. A maturing concept for investigating regulation of and through algorithms(2022) Ulbricht, Lena; Yeung, KarenThis paper offers a critical synthesis of the articles in this Special Issue with a view to assessing the concept of “algorithmic regulation” as a mode of social coordination and control articulated by Yeung in 2017. We highlight significant changes in public debate about the role of algorithms in society occurring in the last five years. We also highlight prominent themes that emerge from the contributions, illuminating what is distinctive about the concept of algorithmic regulation, reflecting upon some of its strengths, limitations, and its relationship with the broader research field. In closing, we argue that the core concept is valuable and maturing. It has evolved into an analytical bridge that fosters cross-disciplinary development and analysis in ways that enrich its early “skeletal” form, thereby enabling careful and context-sensitive analysis of algorithmic regulation in concrete settings while facilitating critical reflection concerning the legitimacy of existing and proposed regulatory regimes.Item The sum of its parts. Analysis of federated byzantine agreement systems(2022) Florian, Martin; Henningsen, Sebastian; Ndolo, Charmaine; Scheuermann, BjörnFederated Byzantine Agreement Systems (FBASs) are a fascinating new paradigm in the context of consensus protocols. Originally proposed for powering the Stellar payment network, FBASs can instantiate Byzantine quorum systems without requiring out-of-band agreement on a common set of validators; every node is free to decide for itself with whom it requires agreement. Sybil-resistant and yet energy-efficient consensus protocols can therefore be built upon FBASs, and the “decentrality” possible with the FBAS paradigm might be sufficient to reduce the use of environmentally unsustainable proof-of-work protocols. In this paper, we first demonstrate how the robustness of individual FBASs can be determined, by precisely determining their safety and liveness buffers and therefore enabling a comparison with threshold-based quorum systems. Using simulations and example node configuration strategies, we then empirically investigate the hypothesis that while FBASs can be bootstrapped in a bottom-up fashion from individual preferences, strategic considerations should additionally be applied by node operators in order to arrive at FBASs that are robust and amenable to monitoring. Finally, we investigate the reported “open-membership” property of FBASs. We observe that an often small group of nodes is exclusively relevant for determining liveness buffers and prove that membership in this top tier is conditional on the approval by current top tier nodes if maintaining safety is a core requirement.Item “Computer says no”. Algorithmic decision support and organisational responsibility(2021) Adensamer, Angelika; Gsenger, Rita; Klausner, Lukas DanielAlgorithmic decision support is increasingly used in a whole array of different contexts and structures in various areas of society, influencing many people’s lives. Its use raises questions, among others, about accountability, transparency and responsibility. While there is substantial research on the issue of algorithmic systems and responsibility in general, there is little to no priorresearch on organisational responsibility and its attribution. Our article aims to fill that gap; we give a brief overview of the central issues connected to ADS, responsibility and decision-making in organisational contexts and identify open questions and research gaps. Furthermore, we describe a set of guidelines and a complementary digital tool to assist practitioners in mapping responsibility when introducing ADS within their organisational context.Item Self-Focused and Other-Focused Health Concerns as Predictors of the Uptake of Corona Contact Tracing Apps. Empirical Study(2021) Große Deters, Fenne; Meier, Tabea; Milek, Anne; Horn, Andrea B.**Background:** Corona contact tracing apps are a novel and promising measure to reduce the spread of COVID-19. They can help to balance the need to maintain normal life and economic activities as much as possible while still avoiding exponentially growing case numbers. However, a majority of citizens need to be willing to install such an app for it to be effective. Hence, knowledge about drivers for app uptake is crucial. **Objective:** This study aimed to add to our understanding of underlying psychological factors motivating app uptake. More specifically, we investigated the role of concern for one’s own health and concern to unknowingly infect others. **Methods:** A two-wave survey with 346 German-speaking participants from Switzerland and Germany was conducted. We measured the uptake of two decentralized contact tracing apps officially launched by governments (Corona-Warn-App, Germany; SwissCovid, Switzerland), as well as concerns regarding COVID-19 and control variables. **Results:** Controlling for demographic variables and general attitudes toward the government and the pandemic, logistic regression analysis showed a significant effect of self-focused concerns (odds ratio [OR] 1.64, P=.002). Meanwhile, concern of unknowingly infecting others did not contribute significantly to the prediction of app uptake over and above concern for one’s own health (OR 1.01, P=.92). Longitudinal analyses replicated this pattern and showed no support for the possibility that app uptake provokes changes in levels of concern. Testing for a curvilinear relationship, there was no evidence that “too much” concern leads to defensive reactions and reduces app uptake. **Conclusions:** As one of the first studies to assess the installation of already launched corona tracing apps, this study extends our knowledge of the motivational landscape of app uptake. Based on this, practical implications for communication strategies and app design are discussed.