Leadership

Integrating artificial intelligence (AI) into workplace processes promises significant efficiency gains, yet organizations face numerous ethical challenges that stakeholders are often initially unaware of—from 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’s non-linear design creates significant usability challenges, indicating a user preference for more structured, linear guidance, especially ...

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.

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.

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.

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.

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.

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 “enabler technology” to help them work “better.” ...

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.