Fachbereich Wirtschaftswissenschaften
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- 2022 (89) (remove)
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Agiles IT-Controlling
(2022)
Während im IT-Projektmanagement agile Methoden seit vielen Jahren in der Praxis Zuspruch finden, werden im IT-Controlling überwiegend noch klassische Methoden eingesetzt. Der Beitrag untersucht die Fragestellung, ob und wie die im IT-Controlling eingesetzten Methoden auch agilen Paradigmen folgen und Methoden des agilen IT-Projektmanagements adaptiert werden können.
Während sich die unternehmerische Arbeitswelt immer mehr in Richtung Agilität verschiebt, verharrt das IT-Controlling noch in alten, klassischen Strukturen. Diese Arbeit untersucht die Fragestellung, ob und inwieweit agile Ansätze im IT-Controlling eingesetzt werden können. Dieser Beitrag ist eine modifizierte Version des in der Zeitschrift „HMD Praxis der Wirtschaftsinformatik“ (https://link.springer.com/article/10.1365/s40702-022-00837-0) erschienenen Artikels „Agiles IT-Controlling“.
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.
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.
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?
Dienstleister
(2022)
Internationale Patienten
(2022)