{"id":103490,"date":"2024-02-20T14:22:19","date_gmt":"2024-02-20T13:22:19","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103490"},"modified":"2025-02-10T17:44:08","modified_gmt":"2025-02-10T16:44:08","slug":"leadership","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/management\/leadership\/","title":{"rendered":"Leadership"},"content":{"rendered":"\n\n\n
\n
\n \n
\n \n \n \n \"Operationalizing\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Operationalizing Ethical AI with tachAId<\/h4>\n
Validating an interactive advisory tool in two manufacturing use cases<\/div>
Pavlos Rath-Manakidis<\/a>, Henry Huick<\/a>, Bj\u00f6rn Kr\u00e4mer<\/a> \n \"ORCID<\/a>, Laurenz Wiskott<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Integrating artificial intelligence (AI) into workplace processes promises significant efficiency gains, yet organizations face numerous ethical challenges that stakeholders are often initially unaware of\u2014from opacity in decision-making to algorithmic bias and premature automation risks. This paper presents the design and validation of tachAId, an interactive advisory tool aimed at embedding human-centered ethical considerations into the development of AI solutions. It reports on a validation study conducted across two distinct industrial AI applications with varying AI maturity. tachAId successfully directs attention to critical ethical considerations across the AI solution lifecycle that might be overlooked in technically-focused development. However, the findings also reveal a central tension: while effective in raising awareness, the tool\u2019s non-linear design creates significant usability challenges, indicating a user preference for more structured, linear guidance, especially ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 50-59 | DOI 10.30844\/I4SE.26.1.48<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Co-Determination\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Co-Determination Dialogues<\/h4>\n
A tool for human-centered AI implementation<\/div>
Manfred Wann\u00f6ffel<\/a> \n \"ORCID<\/a>, Fabian Hoose<\/a> \n \"ORCID<\/a>, Alexander Ranft<\/a>, Claudia Niewerth<\/a> \n \"ORCID<\/a>, Dirk St\u00fcter<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n As part of the regional competence center humAIne, funded by the Federal Ministry of Research, Technology, and Space (BMFTR), a process was developed using co-determination dialogues to establish a common understanding of the challenges involved in the introduction of artificial intelligence (AI) between management, employees, and interest groups. Experiences from project partner companies such as Doncasters Precision Castings in Bochum GmbH (DPC) exemplify how co-determination dialogues not only help to develop legally binding regulations for manageable, operationally anchored, sustainable AI use but also initiate continuous qualification processes for all stakeholder groups in accordance with Articles 4 and 5 of the EU AI Act. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 92-98 | DOI 10.30844\/I4SE.26.1.84<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Pre-Stages\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Pre-Stages of GenAI Governance via Managerial Communication<\/h4>\n
Exploratory findings from SMEs in the Ruhr area<\/div>
Niklas Obermann<\/a> \n \"ORCID<\/a>, Uta Wilkens<\/a> \n \"ORCID<\/a>, Antonia Weirich<\/a> \n \"ORCID<\/a>, Matthias E. Cichon<\/a> \n \"ORCID<\/a>, J\u00fcrgen Mazarov<\/a> \n \"ORCID<\/a>, Bernd Kuhlenk\u00f6tter<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The governance of generative artificial intelligence (GenAI) usage is often described as a formalized reporting system. This neglects the early-stage mechanisms of coping with ethical challenges during the GenAI implementation period. Exploratory empirical findings from the Ruhr area reveal that managerial communicative practices serve as a substitute for missing institutional structures, particularly at an early stage of GenAI implementation in SMEs. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 6-13 | DOI 10.30844\/I4SE.26.1.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Ethical\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Ethical AI in the Workplace Through Value-Based Labels?<\/h4>\n
Lessons learned from applying the VCIO framework to an AI-based assistant<\/div>
Natalie Martin<\/a> \n \"ORCID<\/a>, Tobias Kopp<\/a> \n \"ORCID<\/a>, Natalie Beyer<\/a>, Jochen Wendel<\/a> \n \"ORCID<\/a>, Steffen Kinkel<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The AI Ethics Label represents a promising approach to promoting ethical AI and appropriate trust in AI systems. However, its practical application reveals some challenges due to its conservative assessment approach, limited context sensitivity, lack of benchmarks, and interpretation aids. Improvements are needed to unlock its full potential. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 30-38 | DOI 10.30844\/I4SE.26.1.30<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Multi-Stakeholder\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Multi-Stakeholder AI Ethics in Radiology<\/h4>\n
Implications for integrated technology and workplace design<\/div>
Valentin Langholf<\/a> \n \"ORCID<\/a>, Alexander Ranft<\/a>, Lena Will<\/a>, Robin Denz<\/a> \n \"ORCID<\/a>, Johannes Schwarz<\/a> \n \"ORCID<\/a>, Majd Syoufi<\/a>, Pavlos Rath-Manakidis<\/a>, Marc K\u00e4mmerer<\/a>, Marcus Kremers<\/a>, Axel Mosig<\/a> \n \"ORCID<\/a>, Uta Wilkens<\/a> \n \"ORCID<\/a>, J\u00f6rg Wellmer<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n AI assistance can be seen as a welcome aid in radiology, a highly complex environment characterized by round-the-clock time pressure and quality expectations. However, it must meet high ethical standards from the perspective of both users and patients. It is a challenge to incorporate this human-centered approach into the development and introduction of AI applications. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 136-143 | DOI 10.30844\/I4SE.26.1.128<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"AI\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

AI Skills for Responsible Use<\/h4>\n
Realistic learning environments, critical thinking, and role design in teams<\/div>
Valentin Langholf<\/a> \n \"ORCID<\/a>, Niklas Obermann<\/a> \n \"ORCID<\/a>, Uta Wilkens<\/a> \n \"ORCID<\/a>, Marco Kuhnke<\/a>, Michael Pr\u00fcfer<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence (AI) is changing the world of work. But how can work teams learn to use AI support in a way that delivers speed advantages and ensures consistently high quality? One possible approach is to test it in a workplace-like simulation. Trying it out under realistic conditions shows the role that critical thinking plays. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 100-107 | DOI 10.30844\/I4SE.26.1.92<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Work\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Work Design for Learning and Competence Development<\/h4>\n
Emerging ways of organizing and supporting learning in digitally transformed workplaces<\/div>
Peter Dehnbostel<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Learning and skill-enhancing work designs are essential for new work and organizational concepts such as \u201clearning organizations\u201d and \u201cIndustry 4.0.\u201d Developing and applying criteria for promoting learning and skills development in digitally transformed work environments enables more effective and efficient work processes, makes work more people-centered, and the AI-based future of work more manageable. Digitalization is also introducing work-integrated learning formats such as online communities, learning platforms, and digital cognitive assistance systems that already meet many of these criteria. In the future, designing work environments that promote learning and skills development will become a central task of corporate training and personnel and organizational development. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 6 | Pages 58-64<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"The\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

The Bias of \u201cInstructional Systems for the Disabled\u201d<\/h4>\n
Ethnographic insights from deploying augmented reality in a sheltered workshop<\/div>
David Kostolani<\/a> \n \"ORCID<\/a>, Annemarie Ploss<\/a>, Sebastian Schlund<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The rehumanization of industrial work has emerged as a key focus in Industry 4.0 research, emphasizing the empowerment of human workers amidst advancing automation. Within this re-search, supporting workers with disabilities through digital assistance technologies serves as a prime example of a human-centric approach to industrial engineering. These technologies often claim to enhance productivity, which aims to promote the integration of workers with disabili-ties in industrial roles. But can they genuinely improve their work experience? This ethnograph-ic study presents insights from two years of developing and deploying augmented reality in a sheltered woodworking workshop. Over this period, we engaged in conversations and facilitat-ed over 30 technology sessions with workers with diverse disabilities. Our experiences chal-lenge the narrative of industrial research, in particular with digital instructional systems serving as \u201cenabler technology\u201d to help them work \u201cbetter.\u201d ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 102-110 | DOI 10.30844\/I4SE.25.5.102<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Mechanisms\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Mechanisms of GenAI Governance<\/h4>\n
A case study on the responsible use of GenAI in organizations<\/div>
Niklas Obermann<\/a> \n \"ORCID<\/a>, Daniel Lupp<\/a> \n \"ORCID<\/a>, Uta Wilkens<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Compared to traditional AI systems, generative artificial intelligence (GenAI) introduces user-dependent characteristics that create unique challenges for AI governance in organizations. These challenges are particularly tied to human factors, such as employee attitude, awareness, and skills, which are often neglected by existing governance frameworks. This qualitative case study examines how a manufacturing organization implemented GenAI governance mechanisms to foster the responsible use of this technology. The findings reveal that organizations should adopt a holistic approach, combining structural, procedural, and relational mechanisms to address employee-related aspects of GenAI governance. As a result, this study contributes to the growing field of GenAI governance and provides practical insights for its responsible use in organizations. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 58-64 | DOI 10.30844\/I4SE.25.5.58<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Introduction\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Introduction of Machine Learning in Production<\/h4>\n
An SME-specific, holistic guide<\/div>
Manuel Savadogo<\/a>, Malte Stonis<\/a> \n \"ORCID<\/a>, Peter Nyhuis<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Machine learning offers a wide range of potential, especially in production, and is therefore becoming increasingly important. However, small and medium-sized businesses are lacking guidelines that are specifically tailored to their individual challenges to guide them step-by-step through the process. In conjunction with a potential analysis, the determination of relevant prerequisites and a maturity assessment, this article can serve as a guide for SMEs. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 88-95<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n…<\/span>\n6<\/a>\n\u00bb<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":461,"featured_media":0,"parent":103482,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-103490","page","type-page","status-publish","hentry"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"front-page-entry":false,"post-entry":false,"post-teaser":false,"post-teaser-mobile":false,"post-custom-size":false,"whitepaper-teaser":false,"card-big":false,"card-portrait":false,"card-big-company":false,"gp-listing":false,"1536x1536":false,"2048x2048":false,"woocommerce_thumbnail":false,"woocommerce_single":false,"woocommerce_gallery_thumbnail":false,"dgwt-wcas-product-suggestion":false},"uagb_author_info":{"display_name":"Nimesh Patel","author_link":"https:\/\/industry-science.com\/en\/author\/nimesh\/"},"uagb_comment_info":0,"uagb_excerpt":null,"_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103490","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/users\/461"}],"replies":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/comments?post=103490"}],"version-history":[{"count":2,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103490\/revisions"}],"predecessor-version":[{"id":107782,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103490\/revisions\/107782"}],"up":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103482"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=103490"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}