Purpose-The purpose of this paper is to contribute to a better understanding of governance choice in the area of algorithmic selection. Algorithms on the Internet shape our daily lives and realities. They select information, automatically assign relevance to them and keep people from drowning in an information flood. The benefits of algorithms are accompanied by risks and governance challenges. Design/methodology/approach-Based on empirical case analyses and a review of the literature, the paper chooses a risk-based governance approach. It identifies and categorizes applications of algorithmic selection and attendant risks. Then, it explores the range of institutional governance options and discusses applied and proposed governance measures for algorithmic selection and the limitations of governance options. Findings-Analyses reveal that there are no one-size-fits-all solutions for the governance of algorithms. Attention has to shift to multi-dimensional solutions and combinations of governance measures that mutually enable and complement each other. Limited knowledge about the developments of markets, risks and the effects of governance interventions hampers the choice of an adequate governance mix. Uncertainties call for risk and technology assessment to strengthen the foundations for evidence-based governance. Originality/value-The paper furthers the understanding of governance choice in the area of algorithmic selection with a structured synopsis on rationales, options and limitations for the governance of algorithms. It provides a functional typology of applications of algorithmic selection, a comprehensive overview of the risks of algorithmic selection and a systematic discussion of governance options and its limitations.

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The growing role of alternative modes of regulation (self-and co-regulation) gives rise to major questions about regulatory choice between available governance mechanisms. Strategic policy instruments such as regulatory impact assessment guidelines (RIA) typically suggest assessing the suitability of alternative modes of regulation but they hardly specify assessment criteria. This article identifies contextual factors that should be included in any effort to predict when alternative regulatory arrangements are likely to emerge and to be effective. To demonstrate the value of the approach, it is applied to an analysis of selfregulation in the domain of content-rating in the audiovisual industry.

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