Generic Role Model for the Systematic Development of Internal AI-based Services in Manufacturing

Latest research has shown that one challenge for the development and implementation of Industrial AI-based services is uncertainty of roles and responsibilities. To address this challenge, we developed a generic role model for the systematic development of AI-based services in manufacturing. The role model describes which roles are necessary within the development process of an Industrial AI-based service. Thereby, a distinction is made whether the roles are assigned to the “core team”, the “extended team” or participate in “supporting roles”. Furthermore, the model shows whether the roles are involved in the “Ideation” phase, the “Requirements and design” phase, the “Test” phase or the “Implementation and roll-out” phase. Based on desktop research, semi-structured interviews and expert workshops we identified 22 roles that are relevant to the development and implementation of Industrial AI-based services

Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Development

Despite large progress in Explainable and Safe AI, practitioners suffer from a lack of regulation and standards for AI safety. In this work we merge recent regulation efforts by the European Union and first proposals for AI guidelines with recent trends in research: data and model cards. We propose the use of standardized cards to document AI applications throughout the development process. Our main contribution is the introduction of use-case and operation cards, along with updates for data and model cards to cope with regulatory requirements. We reference both recent research as well as the source of the regulation in our cards and provide references to additional support material and toolboxes whenever possible. The goal is to design cards that help practitioners develop safe AI systems throughout the development process, while enabling efficient third-party auditing of AI applications, being easy to understand, and building trust in the system. Our work incorporates insights from interviews with certification experts as well as developers and individuals working with the developed AI applications.Comment: 54 pages, 1 figure, to be submitte

Menschenzentrierte industrielle Künstliche Intelligenz: Ansätze zur Gestaltung akzeptierter und vertrauenswürdiger KI-basierter Services in der Produktion

Das Ziel der vorliegenden Forschungsarbeit besteht darin, einen Beitrag zur erfolgreichen Gestaltung auf Künstlicher Intelligenz (KI) basierender Services in Produktionsumgebungen zu leisten, der über eine rein technisch-getriebene Entwicklungsperspektive hinausgeht. Dafür wird eine ganzheitliche Betrachtungsperspektive entlang der Ebenen Mensch, Technik und Organisation eingenommen. In einer ersten Analyse wurden Herausforderungen und Erfolgsfaktoren im Zusammenhang mit der Entwicklung, Einführung und dem Betrieb von industriellen KI-basierten Services auf allen Ebenen eines sozio-technischen Systems identifiziert. In einem zweiten Schritt wurden Möglichkeiten zur Stärkung ausgewählter Erfolgsfaktoren oder Lösung spezifischer Herausforderungen definiert, die sowohl menschenzentrierte als auch organisatorische Bereiche betreffen. In anwendungsnahen Forschungsprojekten wurden unter Berücksichtigung der Erfahrungen von Expertinnen und Experten für industrielle KI-Anwendungen zwei Lösungsbereiche ermittelt, die zu einer Optimierung im industriellen Umfeld führen können. Zum einen wurde der Bedarf eines Rollenmodells zur Entwicklung interner KI-basierter Services identifiziert, zum anderen wurde erkannt, dass Ansätze aus dem Bereich Human-Centered Artificial Intelligence (HCAI) ein hohes Potenzial haben, zentrale Herausforderungen der KI-Entwicklung und KI Nutzung im industriellen Kontext zu überwinden. Insbesondere die Anwendung von HCAI Design Prinzipien wird als wertvoll für die industrielle Praxis angesehen. Deutlich wurde aber auch, dass es im breiten Forschungsfeld HCAI einer Kontextualisierung bestehender Ansätze bedarf und eine an die produktionsspezifischen Rahmenbedingungen angepasste Konzeption von Methoden notwendig ist. Auf dieser Erkenntnis aufbauend wurden zwei Modelle konzipiert, die als methodische Unterstützung für primär technisch versierte Entwicklerinnen und Entwickler KI-basierter Services in der Produktion dienen. Dies ist zum einen das „Generische Rollenmodell zur systematischen Entwicklung interner KI basierter Services in der Produktion“ und zum anderen das „Vorgehensmodell zur Nutzung von Design Prinzipien in der ko kreativen Gestaltung menschenzentrierter KI-basierter Services“. Durch die Anwendung der Modelle können zentrale Herausforderungen der KI Entwicklung überwunden und ausgewählte Erfolgsfaktoren gestärkt werden. Das Rollenmodell beschreibt entlang der Entwicklungsphasen eines KI-basierten Services idealtypisch, welche Rollen an der Entwicklung beteiligt werden sollen, welche zentralen Aufgaben die einzelnen Rollen übernehmen und wie intensiv diese in den Entwicklungsprozess eingebunden werden sollen. Das Vorgehensmodell fokussiert die menschenzentrierte Entwicklung KI-basierter Services, indem es durch einen ko-kreativen Ansatz die Anwendung von Design-Prinzipien bei der Gestaltung industrieller KI-basierter Services unterstützt. Beide Modelle fördern die stärkere Einbindung verschiedener Mitarbeitenden, insbesondere auch von Endanwenderinnen und Endanwendern, in den Entwicklungsprozess. Insgesamt trägt die Arbeit dazu bei, industrielle KI basierte Services aus einer ganzheitlichen, menschenzentrierten Perspektive zu betrachten und durch die Anwendung der Modelle die Akzeptanz von KI-basierten Services zu stärken und die Vertrauenswürdigkeit zu erhöhen.The aim of this dissertation is to provide a contribution to the successful design of Artificial Intelligence (AI)-based services in production environments, going beyond a purely technology driven development perspective. A holistic view of people, technology, and organization is taken to achieve this. A first analysis identified challenges and success factors related to the development, implementation, and operation of industrial AI based services at all levels of a socio-technical system. In a second step, possibilities for strengthening selected success factors or addressing specific challenges were identified, covering both human centered and organizational issues. In application-oriented research projects, considering the experience of experts in industrial AI applications, two solution areas have been identified that could lead to optimization in an industrial environment. On the one hand, the need for a role model for the development of internal AI-based services was identified. On the other hand, it was recognized that approaches from the field of Human-Centered Artificial Intelligence (HCAI) have a high potential to overcome the central challenges of AI development and AI application in an industrial context. In particular, the application of HCAI design principles is seen as valuable for industrial practice. However, it also became clear that in the broad field of HCAI research, existing approaches need to be contextualized and methods adapted to the production-specific framework. Building on this insight, two models were designed to provide methodological support for developers of AI-based services in production who are primarily technically experienced. The first is the "Generic role model for the systematic development of internal AI-based services in production". The second is the "Process model for applying design principles in the co-creative design of human-centered IAI based services". By applying the models, key challenges of AI development can be overcome, and selected success factors can be strengthened. Along the development phases of an AI-based service, the Role Model ideally describes which roles should be involved in developing the service, what key tasks each role should take on, and how intensively they should be involved in the development process. The process model focuses on the human-centered development of AI based services by using a co-creative approach to support the application of design principles in the design of industrial AI-based services. Both models promote a greater involvement of different stakeholders, especially end users, in the development process. Overall, the work contributes to a holistic, human-centered view of industrial AI-based services and, through the application of the models, increases the acceptance and trustworthiness of AI-based services

Digital Factory Transformation from a Servitization Perspective: Fields of Action for Developing Internal Smart Services

In recent years, a complex set of dynamic developments driven by both the economy and the emergence of digital technologies has put pressure on manufacturing companies to adapt. The concept of servitization, i.e., the shift from a product-centric to a service-centric value creation logic, can help manufacturing companies stabilize their business in such volatile times. Existing academic literature investigates the potential and challenges of servitization and the associated development of data-based services, so-called smart services, with a view to external market performance. However, with the increasing use of digital technologies in manufacturing and the development of internal smart services based on them, we argue that the existing insights on external servitization are also of interest for internal transformation. In this paper, we identify key findings from service literature, apply them to digital factory transformation, and structure them into six fields of action along the dimensions of people, technology, and organization. As a result, recommendations for designing digital factory transformation in manufacturing companies are derived from the perspective of servitization and developing internal smart services