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Short abstract:

This study investigates the shaping of AI governance through applications in governance frameworks and methods using an STS lens. We discuss 4 frameworks and 5 assessments and how ethics as governance of AI were framed by different involved actors.

Long abstract:

This study probes the diverse articulations of AI governance, focusing on how legal, ethical, and technological frameworks shape these. Many methods to govern AI exist, but the GDPR, AI Act, and Trustworthy AI Ethics guidelines are closing alternatives for AI governance. Parallel to these legal and ethical frameworks, different assessment lists and methods narrow down what AI governance entails.

Employing the lens of STS, the study views AI governance frameworks as mediation (Verbeek, 2006) through artefacts (Davis, 2020), emphasising their role in shaping user interactions and ethical considerations in AI governance. Each framework or method affords certain interactions, specific users and non-users and a selection of moral principles to include or exclude. Relevant social groups (Pinch & Bijker, 1984) are creating methods and frameworks that close what AI governance might mean.

The empirical research consists of projects with an AI governance need for a guideline, framework, or assessment. This resulted in co-creating four frameworks (one federal, two regional and one local) and five assessments of specific AI projects. Our analysis revealed that current AI governance frameworks prioritise certain ethical principles while marginalising others, leading to a homogenised understanding of AI governance. Imposing AI governance frameworks often leads to project owners perceiving them as tedious, mandatory tasks, detracting from the potential of AI governance to offer valuable insights and improvements for overlooked challenges and opportunities in projects.