# German AI Start-Ups and AI Ethics: Using A Social Practice Lens for Assessing and Implementing Socio-Technical Innovation

arXiv: https://arxiv.org/abs/2206.09978 · HTML: https://mlsanity.com/paper/2206.09978/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2022.

## Built on by (most cited first, 11 of 11)

- [From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML](https://mlsanity.com/paper/2210.03535/) (2022)
- [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022)
- [From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape](https://mlsanity.com/paper/2312.10868/) (2023)
- [Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application](https://mlsanity.com/paper/2305.08598/) (2023)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [Machine Learning practices and infrastructures](https://mlsanity.com/paper/2307.06518/) (2023)
- [Articulation Work and Tinkering for Fairness in Machine Learning](https://mlsanity.com/paper/2407.16496/) (2024)
- [Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration](https://mlsanity.com/paper/2604.21554/) (2026)
- [The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology](https://mlsanity.com/paper/2401.10877/) (2024)
- [From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap](https://mlsanity.com/paper/2404.13131/) (2024)
- [What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research](https://mlsanity.com/paper/2608.10431/) (2026)

## Abstract (arXiv)

Within the current AI ethics discourse, there is a gap in empirical research on understanding how AI practitioners understand ethics and socially organize to operationalize ethical concerns, particularly in the context of AI start-ups. This gap intensifies the risk of a disconnect between scholarly research, innovation, and application. This risk materializes acutely as mounting pressures to identify and mitigate the potential harms of AI systems have created an urgent need to assess and implement socio-technical innovation for fairness, accountability, and transparency. Building on social practice theory, we address this need via a framework that allows AI researchers, practitioners, and regulators to systematically analyze existing cultural understandings, histories, and social practices of ethical AI to define appropriate strategies for effectively implementing socio-technical innovations. Our contributions are threefold: 1) we introduce a practice-based approach for understanding ethical AI; 2) we present empirical findings from our study on the operationalization of ethics in German AI start-ups to underline that AI ethics and social practices must be understood in their specific cultural and historical contexts; and 3) based on our empirical findings, we suggest that ethical AI practices can be broken down into principles, needs, narratives, materializations, and cultural genealogies to form a useful backdrop for considering socio-technical innovations.