Employing artificial intelligence (AI) is driven by many expectations and has enormous effects on working environments in the medium term. Besides aims to boost efficiency of work organization, AI is also framed as beacon of hope to make knowledge work more productive. But how can new, AI-based forms of automation constructively complement knowledge work practices? What are the related challenges, and what role do knowledge and basic technical skills play?

Knowledge is key to tap the full potential of AI-based technologies in working environments in a constructive manner: knowledge about their functionality, capabilities but also limits, pitfalls and the related effects on work organization. The research project ties in here and examines how practices of knowledge work alter and, based on this, develops approaches to strengthen critical technology competence for the responsible use of AI-based technologies (critical AI literacy).

 The project grounds on the basic assumption that new forms of automation arising from AI can reinforce risks of automation bias, affected by a number of factors. Automation Bias has different but interrelated levels: on the one hand, the human tendency to "blindly" trust in technologies, which depends, among other things, on technical competence. On the other hand, technical complexity and opacity, which complicates the ability of individuals to comprehend how technology functions and to correctly interpret the outcome of technical processes. This risk is particularly higher in cases where the usage context of a technology is not in accordance with actual practices of work, i.e., when there is a mismatch between the functionality of a technology and practical requirements in specific contexts. Besides unrecognized technical errors this can lead to poor or wrong decisions and ineffective workflows.

The project analyzes these factors and their role in working practices in depth by conducting different case studies in selected areas and qualitative interviews with experts and practitioners to identify central influencing factors, problem areas and relevant criteria for key competencies. Based on the findings, a practical guideline for strengthening critical AI literacy in companies will be developed to raise problem awareness and a constructive, socially acceptable use of digitization and AI-based technologies in companies.



  • Strauß, S. (2021). “Don’t let me be misunderstood” - Critical AI literacy for the constructive use of AI technology. Tatup - Zeitschrift Für Technikfolgenabschätzung In Theorie Und Praxis, 30, 44-49. doi:10.14512/tatup.30.3.44
  • Stefan Strauß,. (2021). Deep Automation Bias: How to Tackle a Wicked Problem of AI?. Big Data And Cognitive Computing (Bdcc), 2021, 1-14. doi:10.3390/bdcc5020018
  • 1

Conference papers/lectures


  • 25/07/2022 , Karlsruhe
    Stefan Strauß: 
    "Something wicked this way comes" – the role of deep automation bias for critical AI literacy
    5th European Technology Assessment Conference (ETAC5) July 25-27
  • 1




01/2023 - 06/2024

Project team