AI Fairness at Subgroup Level – A Structured Literature Review

AI applications in practice often fail to gain the required acceptance by stakeholders due to unfairness issues. Research has primarily investigated AI fairness on individual or group levels. However, increasing research indicates shortcomings in this two-fold view. Particularly, the non-inclusion of the heterogeneity within different groups leads to increasing demand for specific fairness consideration at the subgroup level. Subgroups emerge from the conjunction of several protected attributes. An equal distribution of classified individuals between subgroups is the fundamental goal. This paper analyzes the fundamentals of subgroup fairness and its integration in group and individual fairness. Based on a literature review, we analyze the existing concepts of subgroup fairness in research. Our paper raises awareness for this primary neglected topic in IS research and contributes to the understanding of AI subgroup fairness by providing a deeper understanding of the underlying concepts and their implications on AI development and operation in practice

Task delegation from AI to humans: A principal-agent perspective

Increasingly intelligent AI artifacts in human-AI systems perform tasks more autonomously as entities that guide human actions, even changing the direction of task delegation between humans and AI. It has been shown that human-AI systems achieve better results when the AI artifact takes the leading role and delegates tasks to a human rather than the other way around. This study presents phenomena, conflicts, and challenges that arise in this process, explored through the theoretical lens of principal-agent theory (PAT). The findings are derived from a systematic literature review and an exploratory interview study and are placed in the context of existing constructs of PAT. Furthermore, this article paper identifies new causes of tensions that arise specifically in AI-to-human delegation and calls for special mechanisms beyond the classical solutions of PAT. The paper thus contributes to the understanding of autonomous AI and its implications for human-AI delegation

Unlocking the power of generative AI models and systems such asGPT-4 and ChatGPT for higher education

Generative AI technologies, such as large language models, have the potential to revolutionize much of our higher education teaching and learning. ChatGPT is an impressive, easy-to-use, publicly accessible system demonstrating the power of large language models such as GPT-4. Other compa- rable generative models are available for text processing, images, audio, video, and other outputs and we expect a massive further performance increase, integration in larger software systems, and diffusion in the coming years. This technological development triggers substantial uncertainty and change in university-level teaching and learning. Students ask questions like: How can ChatGPT or other artificial intelligence tools support me? Am I allowed to use ChatGPT for a seminar or final paper, or is that cheating? How exactly do I use ChatGPT best? Are there other ways to access models such as GPT-4? Given that such tools are here to stay, what skills should I acquire, and what is obsolete? Lecturers ask similar questions from a different perspective: What skills should I teach? How can I test students competencies rather than their ability to prompt generative AI models? How can I use ChatGPT and other systems based on generative AI to increase my efficiency or even improve my students learning experience and outcomes? Even if the current discussion revolves around ChatGPT and GPT-4, these are only the forerunners of what we can expect from future generative AI-based models and tools. So even if you think ChatGPT is not yet technically mature, it is worth looking into its impact on higher education. This is where this whitepaper comes in. It looks at ChatGPT as a contemporary example of a conversational user interface that leverages large language models. The whitepaper looks at ChatGPT from the perspective of students and lecturers. It focuses on everyday areas of higher education: teaching courses, learning for an exam, crafting seminar papers and theses, and assessing students learning outcomes and performance. For this purpose, we consider the chances and concrete application possibilities, the limits and risks of ChatGPT, and the underlying large language models. This serves two purposes: First, we aim to provide concrete examples and guidance for individual students and lecturers to find their way of dealing with ChatGPT and similar tools. Second, this whitepaper shall inform the more extensive organizational sensemaking processes on embracing and enclosing large language models or related tools in higher education. We wrote this whitepaper based on our experience in information systems, computer science, management, and sociology. We have hands-on experience in using generative AI tools. As professors, postdocs, doctoral candidates, and students, we constantly innovate our teaching and learning. Fully embracing the chances and challenges of generative AI requires adding further perspectives from scholars in various other disciplines (focusing on didactics of higher education and legal aspects), university administrations, and broader student groups. Overall, we have a positive picture of generative AI models and tools such as GPT-4 and ChatGPT. As always, there is light and dark, and change is difficult. However, if we issue clear guidelines on the part of the universities, faculties, and individual lecturers, and if lecturers and students use such systems efficiently and responsibly, our higher education system may improve. We see a greatchance for that if we embrace and manage the change appropriately

Gateways to Artificial Intelligence: Developing a Taxonomy for AI Service Platforms

Artificial Intelligence (AI) carries the potential to drive innovation in many parts of today’s business environment. Instead of building AI capabilities in-house, some organizations turn towards an emergent phenomenon: AI service platforms. However, as a novel concept in both research and practice, a systematic characterization of AI service platforms is missing. To address this gap, we define the concept of AI service platforms and develop a comprehensive taxonomy. Therefore, we rely on existing literature, 14 expert interviews, and a sample of 31 AI service platforms. Our contribution is threefold: First, our taxonomy systematically structures essential properties of AI service platforms, guiding future research and management practice. Second, we derive three generic motives of AI service platforms. Third, we contribute to the literature by critically discussing to what extent AI service platforms fit into the existing academic discourse on digital platforms and elaborate on future research directions

The Rise of the Machines: Conceptualizing the Machine Economy

Recently a novel phenomenon, the machine economy, experiences rapidly increasing recognition from both research and practice. However, we still lack a thorough conceptual understanding of its driving technologies and their interrelations. This hampers the incorporation of the machine economy in today’s organizations to unleash its full potential. Therefore, we set out with a conceptual research approach. First, we investigate the characteristics of the central technologies, the Internet of Things (IoT), Artificial Intelligence (AI), and Blockchain (BC). Second, we examine the bilateral technology interrelations to explicate their synergistic interplay. Third, we shed light on their trilateral technology convergence and conflate our reasoning into a holistic conceptual model. Finally, we demonstrate the machine economy’s real-world applicability with three exemplary instantiations. Throughout our research approach, we observe the machine economy concept through two theoretical lenses: the theory of selfadaptive systems and the actor-network theory