Autor: Valentin Langholf

I4S 1/2026: Applied AI Ethics in the Workplace

I4S 1/2026: Applied AI Ethics in the Workplace

A shared responsibility — from radiology and speech therapy to assembly
AI ethics in the workplace is everyone’s responsibility. It requires accountability from companies as a whole and conscious action from individuals—whether developers or users, managers or employees. Key issues revolve around ethical AI skills and questions of governance and employee representation. How will the world of work change, from radiology and speech therapy to assembly and quality control?
Multi-Stakeholder AI Ethics in Radiology

Multi-Stakeholder AI Ethics in Radiology

Implications for integrated technology and workplace design
Valentin Langholf ORCID Icon, Alexander Ranft, Lena Will, Robin Denz ORCID Icon, Johannes Schwarz ORCID Icon, Majd Syoufi, Pavlos Rath-Manakidis, Marc Kämmerer, Marcus Kremers, Axel Mosig ORCID Icon, Uta Wilkens ORCID Icon, Jörg Wellmer ORCID Icon
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.
Industry 4.0 Science | Volume 42 | Edition 1 | Pages 136-143 | DOI 10.30844/I4SE.26.1.128
AI Skills for Responsible Use

AI Skills for Responsible Use

Realistic learning environments, critical thinking, and role design in teams
Valentin Langholf ORCID Icon, Niklas Obermann ORCID Icon, Uta Wilkens ORCID Icon, Marco Kuhnke, Michael Prüfer
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.
Industry 4.0 Science | Volume 42 | Edition 1 | Pages 100-107 | DOI 10.30844/I4SE.26.1.92
Pathways to Responsible Use of AI at Work

Pathways to Responsible Use of AI at Work

An organizational change perspective
Valentin Langholf ORCID Icon, Uta Wilkens ORCID Icon, Daniel Lupp ORCID Icon, Niklas Obermann ORCID Icon
The integration of AI in Industry 4.0 is steadily increasing. Applications include both single-purpose and generative AI systems in operation practices as well as training approaches. In addition to the technical challenges posed by these systems, organizations need to assess, plan and support the organizational changes associated with technology integration.
Industry 4.0 Science | Volume 40 | 2024 | Edition 5 | Pages 58-66 | DOI 10.30844/I4SE.24.5.58
Towards a maturity model of human-centered AI – A reference for AI implementation at the workplace

Towards a maturity model of human-centered AI – A reference for AI implementation at the workplace

Prof. Dr. Annette Kluge, Valentin Langholf ORCID Icon, Greta Ontrup, Prof. Dr. Uta Wilkens
In our paper we present first performance measurement results of a digital simulation laboratory, which is applied in the context of industrial front-end team training. The design of the simulation laboratory is oriented towards an Escape Room. First, we situate the presented approach within existing competency understandings and accompanying training approaches in the context of Industry 4.0 Performance measurement for front-end training has been a challenge in this context so far, since performance, unlike in the back-end, is not attributable to specific production results, but becomes visible on a superior process level. Building on the competency facets of complexity management, self-reflection, creative problem solving, and cooperation (Wilkens et al., 2017) as well as action implementation (Heyse & Erpenbeck, 2009), the performance measurement presented addresses the question which individual competencies have an impact on team performance in the simulation scenario. Preliminary ...
Industry 4.0 Science | 2021 | | DOI 10.30844/wgab_2021_11