The increasing integration of algorithms into various aspects of society has raised concerns about potential biases, ethical implications, and the lack of transparency governing decision-making processes, which in turn led to a call for increased oversight. This paper will contextualise the call for increased ethical governance and oversight of algorithmic systems used within the Dutch public sector, by focusing on the aim of the proposed governance mechanisms and instruments. 

Using the Social Construction of Technology (SCOT) framework, this project aims to create an understanding of how oversight and governance mechanisms for algorithmic systems in the Dutch public sector are shaped by social negotiations, values, and power dynamics, and not merely the technical aspects of the algorithms. Different stakeholders – e.g. policy makers, regulatory bodies, think tanks, and researchers – may advocate for diverse approaches to ensure transparency, accountability, and fairness in algorithmic decision-making. Media articles, parliamentary documents, and advisory reports are used to unravel the social construction of instruments meant for governing or overseeing (the use of) algorithms. By combining the insights from these sources, the multifaceted dynamics influencing the construction of oversight mechanisms can be discerned. The language used, the framing of issues, and the actors involved in these documents reveal societal values, power structures, and ongoing negotiations surrounding the development of oversight frameworks.

The findings of this research will include an overview of the different stakeholders with regard to algorithmic governance in the Netherlands, the way in which they position themselves on the topic, which values they deem essential in the governance of algorithmic systems, and how this relates to other stakeholders. Moreover, the analysis of parliamentary documents and debates will provide insights into the legislative responses and policy developments related to algorithmic oversight within the public sector. By understanding the policy landscape, this research aims to contribute to the ongoing discourse on algorithmic accountability and the formulation of effective governing mechanisms.