Benjamin van Giffen
Title
Prof. Dr.
Last Name
van Giffen
First name
Benjamin
Email
benjamin.vangiffen@unisg.ch
Phone
+41 71 224 3635
27 results
Now showing 1 - 10 of 27
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Item type:Publication, Beyond control? Governing AI in organizations for responsible useEnsuring the responsible use of artificial intelligence (AI) has become a central concern for researchers, business leaders, and policymakers. There is a broad consensus that governing AI at the organizational level is crucial. While the existing literature acknowledges that responsible AI use depends on institutional structures and human agency, it is divided over which factor is decisive. We take an integrative perspective and examine how structure and agency interact when organizations govern AI for responsible use. To this end, we enrich Giddens' structuration theory with ethics (i.e., deontology, consequentialism, virtue ethics) and conceptualize AI governance as reflexive organizational praxis. We illustrate our analytical framework with the case of OTTO, Germany's largest e-commerce company. We conclude that while AI governance cannot define the responsible use of AI, organizations can scaffold how their members reason about whether and how AI should be used in a specific social context.Type:journal articleJournal:European Management Journal - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Responsible Use of Artificial Intelligence as Continuous Proportionalization: Fashion Image Generation at OTTOThe inherent ambivalence of machine learning-based artificial intelligence (AI) technologies makes ensuring their responsible use a pressing concern. While the literature converges on the importance of governance at the organizational level, uncertainty remains about what responsible (use of) AI actually is. We conceptualize responsible AI as both a process and outcome of social evaluation. We propose a model (“continuous proportionalization”) that explains how organizations construct collective interpretations of responsible AI along the dimensions of legitimacy, suitability, necessity, and proportionality. We illustrate the model through a case study of AI-based fashion image generation at Germany’s largest e-commerce company, OTTO.Type:journal articleJournal:Swiss Journal of BusinessVolume:80Issue:1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How Audi Scales Artificial Intelligence in Manufacturing(2024); ; Scopus© Citations 10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How Boards of Directors Govern Artificial Intelligence(2023); Helmuth LudwigArtificial intelligence is top of mind, even for nontechnical business executives and board members. However, the majority of boards struggle to understand the implications of AI for their businesses and their role in governing it. We describe how some boards are addressing AI and identify four groups of board-level AI governance issues. We provide examples of effective board-level AI governance practices for each group of issues and make recommendations for establishing board-level AI governance.Type:journal articleJournal:MIS Quarterly ExecutiveVolume:22Issue:4Scopus© Citations 20 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How Siemens Democratized Artificial Intelligence(2023); Ludwig, HelmuthMany firms aspire to generate business value with artificial intelligence (AI) but struggle to move beyond pilots and prototypes. Based on an in-depth case study, we describe how Siemens has leveraged AI democratization to identify, realize and scale AI use cases by integrating the unique skills of domain experts, data scientists and IT professionals. From the lessons learned at Siemens, we provide recommendations for building this organizational capability and effectively addressing the challenges of adopting the latest AI technologies.Type:journal articleJournal:MIS Quarterly ExecutiveVolume:22Issue:1Scopus© Citations 39 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Engineering AI-Enabled Computer Vision Systems: Lessons From Manufacturing(2022); Scopus© Citations 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Digitale Plattformen in der Praxis – Einsatz- und Entwicklungsmodelle(Springer Fachmedien, 2022-08); ; Galeno, GianlucaType:journal articleJournal:HMD Praxis der Wirtschaftsinformatik - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods(2022); ; Over the last decade, the importance of machine learning increased dramatically in business and marketing. However, when machine learning is used for decision-making, bias rooted in unrepresentative datasets, inadequate models, weak algorithm designs, or human stereotypes can lead to low performance and unfair decisions, resulting in financial, social, and reputational losses. This paper offers a systematic, interdisciplinary literature review of machine learning biases as well as methods to avoid and mitigate these biases. We identified eight distinct machine learning biases, summarized these biases in the cross-industry standard process for data mining to account for all phases of machine learning projects, and outline twenty-four mitigation methods. We further contextualize these biases in a real-world case study and illustrate adequate mitigation strategies. These insights synthesize the literature on machine learning biases in a concise manner and point to the importance of human judgment for machine learning algorithms.Type:journal articleJournal:Journal of Business ResearchVolume:Vol. 144Scopus© Citations 211 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Management von Künstlicher Intelligenz in UnternehmenType:journal articleJournal:HMD Praxis der WirtschaftsinformatikVolume:57Issue:1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Was Unternehmen von der Videospieleindustrie für die Gestaltung der Digital Customer Experience lernen könnenType:journal articleJournal:HMD Praxis der WirtschaftsinformatikVolume:54Issue:5
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