Stefan Schmager

• Grøder, Charlotte Husom; Schmager, Stefan; Parmiggiani, Elena; Vassilakopoulou, Polyxeni; Pappas, Ilias & Papavlasopoulou, Sofia (2026). Responsible AI in Information Systems Education: Impact of Experiential Learning in Practice. Scandinavian Journal of Information Systems (SJIS). ISSN 0905-0167. 38(1), s. 83–116. Fulltekst i vitenarkiv Vis sammendrag

  Responsible Artificial Intelligence (RAI) is an emerging concern for public and private organizations. It is also gaining traction within the Information Systems (IS) discourse. RAI is predicated on AI technologies' ethical, transparent, and accountable deployment, aligning with societal values, norms, and expectations. The conundrum facing IS education is the integration of the expanding RAI concerns and research into pedagogical strategies that introduce students to this nascent subject. One way to provide students with practical and comprehensive educational experiences is by engaging them in experiential learning, which emphasizes active and reflective engagement with real-world problems. However, research on IS education focusing on RAI is currently limited. We ask: How can experiential learning support students’ understanding of Responsible AI? We draw on an empirical, qualitative study of the design, implementation, and evaluation of a course on RAI applied to the context of public welfare services. The study was conducted over two semesters (2022 and 2023) with 49 students in total. We adopt an experiential learning paradigm and propose a course design that fosters multidisciplinary, problem-oriented learning about RAI. Our protocol promotes continuous learning by building an arena for the students to reflect on the learning process and growing awareness of RAI.

• Schmager, Stefan; Pappas, Ilias & Vassilakopoulou, Polyxeni (2025). Human-Centered AI Design Principles for the Public Service Domain: An Action Design Research Study. I Bui, Tung (Red.), Proceedings of the 58th Hawaii International Conference on System Sciences (HICSS), Hilton Waikoloa Village, January 7-10, 2025. University of Hawai'i at Manoa. ISSN 9780998133188. s. 5509–5518. doi: 10.24251/HICSS.2025.661. Fulltekst i vitenarkiv Vis sammendrag

  Most existing design and development guidelines for Human-Centered AI primarily cater to a commercial context, they are not tailored to the specific needs of public services. This paper presents an Action Design Research study proposing public service-specific design principles for Human-Centered AI. The design principles are informed by multiple iterations of empirical research with citizens and public service employees acknowledging the multi-stakeholder nature of Human-Centered AI. The study recognizes an evolving understanding towards prioritizing human values and well-being in AI technologies. Furthermore, it considers the relationship between technology features in the public sector and citizens' needs to ensure that AI systems are developed with a commitment to fostering public trust and welfare. The study contributes to theory and practice by advancing the scholarly discourse on Human-Centered AI and informing AI strategies and implementations within the public sector.

• Schmager, Stefan; Pappas, Ilias & Vassilakopoulou, Polyxeni (2025). Understanding Human-Centred AI: a review of its defining elements and a research agenda. Behaviour and Information Technology. ISSN 0144-929X. 44(15), s. 3771–3810. doi: 10.1080/0144929X.2024.2448719. Fulltekst i vitenarkiv Vis sammendrag

  The rapid advancements in artificial intelligence (AI) have ushered in a new era of innovative applications, while also prompting concerns regarding risks and adverse consequences. In light of the growing interest in comprehending AI's impact on society and its alignment with human values and needs, Human-Centred Artificial Intelligence (HCAI) has emerged as a potential approach to address questions and concerns. In this Systematic Literature Review, we aim to contribute to conceptual clarity around the definition, conceptualisation, and implementation of HCAI. The first part of our review addresses how HCAI is defined in the existing literature, culminating in a novel comprehensive HCAI definition. Subsequently, we delve into the identified constituent elements of HCAI, namely ‘purpose’, ‘values’, and ‘properties’. Purposes include augmentation, AI autonomy, and automation. Values relate to ethics, safety, and performance. Properties cover oversight, comprehension, and integrity. The third part of the review explores Human-Centred Design processes, methods, and tools and their applicability for HCAI. In conclusion, we discuss the characteristics and critiques of HCAI and provide a research agenda. This literature review contributes to advancing the discourse on HCAI, thus enhancing human welfare and societal well-being.

• Schmager, Stefan; Gupta, Samrat; Pappas, Ilias & Vassilakopoulou, Polyxeni (2024). Designing for AI Transparency in Public Services: A User-Centred Study of Citizens’ Preferences. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14720, s. 237–253. doi: 10.1007/978-3-031-61315-9_17. Fulltekst i vitenarkiv
• Schmager, Stefan; Pappas, Ilias & Vasilakopoulou, Polyxeni (2024). Citizens-Focused Design Principles for Human-Centered AI in Public Services, AMCIS 2024 Proceedings. Association for Information Systems (AIS). ISSN 9781958200117. Fulltekst i vitenarkiv Vis sammendrag

  Artificial Intelligence (AI) technologies undergo rapidly increasing integration in all areas of everyday life, including healthcare, employment, and public services. To reconcile abstract theoretical concepts with the practical realities of AI development and deployment in the public sector, this study formulates Human-Centered AI (HCAI) design principles, recognizing the unique aspects of the public sector. The study argues for bespoke AI design and implementation strategies that prioritize societal needs over commercial interests and incorporate citizen perspectives into the design principles. The study employs Action Design Research and draws from Social Contract Theory, aiming to advance the dialogue on responsible AI practices within the public sector. These principles offer valuable insights for both academics and practitioners, bridging the gap between theory and practice, enriching the information systems discipline, and fostering a deeper understanding of a socially responsible AI design and deployment in public services.

• Schmager, Stefan; Grøder, Charlotte Husom; Parmiggiani, Elena; Pappas, Ilias & Vassilakopoulou, Polyxeni (2024). Exploring citizens' stances on AI in public services: A social contract perspective. Data & Policy. 6. doi: 10.1017/dap.2024.13. Fulltekst i vitenarkiv Vis sammendrag

  This paper explores citizens’ stances toward the use of artificial intelligence (AI) in public services in Norway. Utilizing a social contract perspective, the study analyzes the government–citizen relationship at macro, meso, and micro levels. A prototype of an AI-enabled public welfare service was designed and presented to 20 participants who were interviewed to investigate their stances on the described AI use. We found a generally positive attitude and identified three factors contributing to this: (a) the high level of trust in government (macro level); (b) the balanced value proposition between individual and collective needs (meso level); and (c) the reassurance provided by having humans in the loop and providing transparency into processes, data, and model’s logic (microlevel). The findings provide valuable insights into citizens’ stances for socially responsible AI in public services. These insights can inform policy and guide the design and implementation of AI systems in the public sector by foregrounding the government–citizen relationship.

• Eriksson, Christer; Olsen, Kristian Mareno Matre; Schmager, Stefan; Pappas, Ilias & Vassilakopoulou, Polyxeni (2023). Human-AI Collaboration in Public Services: The Case of Sick Leave Case Handling. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14316, s. 41–53. doi: 10.1007/978-3-031-50040-4_4. Fulltekst i vitenarkiv Vis sammendrag

  This research paper explores Human-AI Collaboration in public services, focusing on the needs and understanding of AI among caseworkers in a public welfare organisation dealing with sick leave cases. Conducting a foresight study with 19 caseworkers we identify roles that AI systems can take contributing to improved services for citizens, more fulfilling everyday work for caseworkers and more efficient use of public resources. Our research delves into the entirety of tasks involved in sick leave case handling expanding beyond the digital touchpoints between humans and AI. It goes beyond human-AI interaction to encompass the broader scope of collaboration between humans and machines as joint cognitive systems and suggests thinking of AI as a collaborator that undertakes specific roles.

• Arnarsson, Robert Thor; Ellingsen, Mats; Pham, Quynh-Anh Nguyen; Sterkenburg, Jarl Joar Van; Cappelen, Gard & Hochwarter, Stefan [Vis alle 7 forfattere av denne artikkelen] (2023). Facilitating Responsible Use of Artificial Intelligence in the Public Sector Through Guidelines for Human- AI Interaction. Reports of the European Society for Socially Embedded Technologies. doi: 10.48340/ihc2023_pd029. Fulltekst i vitenarkiv Vis sammendrag

  When developing Artificial Intelligence (AI) systems and implementing them in public services, guidelines for human-AI interaction can be instrumental in ensuring responsible use of these systems and AI intelligibility and responsibility, protecting the citizens’ rights and interests. Although there are as of yet no such guidelines established that are directed at the public sector, tech companies like Microsoft have established guidelines for the commercial sector. To investigate how commercial guidelines may help guide the development of public services that utilize AI, we have explored how Microsoft Human-AI Interaction Guidelines can be amended and used to guide the development of a sick leave duration prediction service. By developing a web application prototype for the specific case and conducting usability testing, we found that the guidelines needed amendments to better ensure responsible use of AI in a public service. Three considerations were discovered to be important when amending them: (i) the way users interact with the AI system, (ii) the diversity of citizens making use of the service, and (iii) the public service context.

• Schmager, Stefan; Grøder, Charlotte Husom; Parmiggiani, Elena; Pappas, Ilias & Vasilakopoulou, Polyxeni (2023). What do citizens think of AI adoption in public services? Exploratory research on citizen attitudes through a social contract lens. I NN, NN (Red.), 56th Hawaii International Conference on System Sciences (HICSS 2023). ScholarSpace. ISSN 9780998133164. s. 4472–4481. Fulltekst i vitenarkiv Vis sammendrag

  The adoption of Artificial Intelligence (AI) by the public sector has the potential to improve service delivery. However, the risks related to AI are significant and citizen concerns have halted several AI initiatives. In this paper we report findings from an empirical study on citizens´ attitudes towards AI use in public services in Norway. We found a generally positive attitude and identified three factors contributing to this: a) the high level of trust in government; b) the reassurance provided by having humans in the loop; c) the perceived transparency into processes, data used for AI models and models´ inner workings. We interpret these findings through the lens of social contract theory and show how the introduction of AI in public services is subject to the social contract power dynamics. Our study contributes to research by foregrounding the government-citizen relationship and has implications for public sector AI practice.

• Schmager, Stefan (2022). FROM COMMERCIAL AGREEMENTS TO THE SOCIAL CONTRACT: HUMAN-CENTERED AI GUIDELINES FOR PUBLIC SERVICES, The 14th Mediterranean Conference on Information Systems (MCIS 2022). Acting in the digital society: moving towards a sustainable future. Association for Information Systems (AIS). ISSN 9788887173055. Fulltekst i vitenarkiv
• Grøder, Charlotte Husom; Schmager, Stefan; Parmiggiani, Elena; Vassilakopoulou, Polyxeni; Pappas, Ilias & Papavlasopoulou, Sofia (2022). Educating about Responsible AI in IS: Designing a course based on Experiential Learning. I BohMann, Tilo; Hui, Kai-Lung & Venkatesh, Viswanath (Red.), ICIS 2022 Proceedings. Association for Information Systems (AIS). ISSN 9781958200032. Fulltekst i vitenarkiv Vis sammendrag

  Responsible AI (RAI) is an emerging topic in the Information Systems (IS) literature. RAI entails ensuring ethical, transparent, and accountable use of AI technologies in line with societal values, expectations, and norms. The challenge for research on IS education at university level is to accompany the growing research on RAI with approaches to educate students about this emerging theme. Research on IS education on responsible AI remains scarce to date, however. We ask: How can we design a course to educate students about responsible AI? We build on earlier research and an experiential learning-based approach to propose a course design promoting students’ multidisciplinary, problem-based learning about RAI applied to the case of public welfare services. Our study is based on participatory observations of student groups and group interviews after a project, acting as an arena where the students could reflect on the learning process and evolving awareness of RAI.

• Schmager, Stefan & Sousa, Sónia (2021). A Toolkit to Enable the Design of Trustworthy AI, HCI International 2021 - Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings. Springer Nature. ISSN 9783030909635. doi: 10.1007/978-3-030-90963-5_41. Fulltekst i vitenarkiv

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• Schmager, Stefan & Vassilakopoulou, Polyxeni (2024). The Citizens´ Right to Know: Empirical Insights for Human Centered AI Transparency. Fulltekst i vitenarkiv
• Schmager, Stefan & Vassilakopoulou, Polyxeni (2024). Depunctualizing and Punctualizing AI: A Critical Approach to Transparency and AI Blackboxes within Information Infrastructures. Fulltekst i vitenarkiv
• Schmager, Stefan (2024). Human-Centered AI for the public sector: Towards a framework for communicating AI use in public services. Fulltekst i vitenarkiv
• Schmager, Stefan; Pappas, Ilias & Vassilakopoulou, Polyxeni (2023). Defining Human-Centered AI: a Comprehensive Review of HCAI Literature. Fulltekst i vitenarkiv
• Schmager, Stefan (2023). AI and the EU – Troubling Times Ahead or an Age of Opportunity. Fulltekst i vitenarkiv Vis sammendrag

  In this episode, we will discuss A.I. (Artificial Intelligence) and uncover the current developments and trends related to this encroaching phenomenon. Are we facing a future where we will all succumb to ChatGPT and our critical thinking will be replaced by sporadically clever algorithms, thus leading to an inevitable downfall for academia? Should we be worried about the increased introduction of A.I. into our everyday lives, where human accountability is being replaced by unaccountable software and bots? And most importantly: How is the EU positioning itself towards A.l. and how does it fare against global tech giants such as China and the USA? All of these issues will be discussed in today's episode. We are joined by Stefan Schmager, PhD Research Fellow in Information Systems at the University of Agder, and by Frans af Malmborg, PhD Research Fellow in Political Science and Management at the University of Agder.

• Schmager, Stefan (2025). Human-Centered Artificial Intelligence: Design Principles for Public Services. Universitetet i Agder. ISSN 9788284272559. Fulltekst i vitenarkiv Vis sammendrag

  Artificial Intelligence (AI) technologies increasingly integrate into all areas of everyday life, including healthcare, employment, and public services. But as much as AI is lauded as a great white hope for enabling humans to approach societal challenges, there are at least as many dystopian futures, describing gloom and doom scenarios. This ever-widening impact has spawned discussions, considerations, and warnings about responsible, ethical, and sustainable implementation of such advanced technologies. In light of the growing interest in comprehending AI's impact on society and its alignment with human values and needs, Human Centered Artificial Intelligence (HCAI) has emerged as a promising approach to address such questions and concerns. HCAI is frequently used in the discourse on how to guide the development and deployment of AI in responsible and trustworthy ways. A human-centered approach to AI foregrounds human needs and values, fostering the understanding that technology is created to support humans, not rendering them superfluous. To operationalize specific recommendations about the fundamental nature and structure of an HCAI system, design principles have been established as a suitable tool across various disciplines such as engineering, economics, and information systems. Design principles can guide the implementation of AI systems ensuring that systems are developed with a clear understanding of their social implications, equipped to handle complex ethical dilemmas, and capable of delivering balanced benefits to both individuals and society as a whole. They aim to bridge the gap between abstract theoretical concepts and practical implementation, guiding the development and deployment of AI technologies within these companies' spheres of influence and beyond. Major technology actors including Microsoft, Apple, and Google are claiming to promote a responsible approach to AI by providing their own set of guidelines and recommendations. However, these guidelines inherit the profit-oriented pretext of their creators and are therefore mostly oriented toward commercial contexts. This commercial focus leaves a significant gap for guidance tailored to public services, where the stakes are uniquely high. This leads to the overarching research question of this doctoral research: What are Human-Centered AI Design Principles for public services? This doctoral research presents an Action Design Research study, approaching HCAI in public services through a Social Contract Theory lens. It draws on empirical investigations to explore both citizens' perspectives on AI usage in public services and the understanding, needs, and expectations of public servants. By acknowledging the multi-stakeholder nature of HCAI, this research highlights the complex dynamics between citizens, public servants, and AI systems in the public sector. The study recognizes an evolving understanding of prioritizing human values and well-being in AI technologies, specifically considering the relationship between citizens and governmental organizations in the context of AI systems in the public sector. It highlights the need to ensure that such AI systems are developed with a commitment to fostering societal and individual benefit and welfare. To reconcile abstract theoretical concepts with the practical realities of AI development and deployment in the public sector, HCAI design principles are formulated to contribute to theory and practice by advancing the scholarly discourse on HCAI and informing AI strategies and implementations within the public sector.

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