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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Algorithms have come to shape our daily lives and realities. They change the perception of the world, affect our behavior by influencing our choices, and are an important source of social order. Algorithms on the Internet have significant economic implications in newly emerging markets and for existing markets in various sectors. A wide range of our daily activities in general and our media consumption in particular are increasingly shaped by algorithms operating behind the scenes: the selection of online news via search engines and news aggregators, the consumption of music and video entertainment via recommender systems, the choice of services and products in online shops and the selection of status messages displayed on social online networks are the most prominent examples of this omnipresent trend. Algorithms suggest friends, news, songs and travel routes. Moreover, they automatically produce news articles and messages, they calculate scorings of content and people, and are employed to observe our behavior and interests as well as to predict our future needs and actions. By assigning relevance to certain pieces of information they keep consumers, companies and authorities from drowning in a growing flood of information and online data. At the same time, they mine and construct realities, guide our actions and thereby determine the economic success of products and services. Algorithms form the technofunctional basis of new services and business models that economically challenge traditional industries and business strategies. These economic changes and challenges are accompanied by and interact with significant social risks such as manipulation and bias, threats to privacy and violations of intellectual property rights that compromise the economic and social welfare effects of algorithmic selection applications. This rapidly growing Internet phenomenon is here called 'algorithmic selection'. It is a central and structuring bundle of Internet innovations in digital economies. Algorithmic selection is embedded in a variety of Internet-based services and is applied for numerous purposes. Although their modes of operation differ in detail, all of these applications are characterized by a common basic functionality: They automatically select information Management Research Paper No. 2013-4, online available http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2225359 (accessed 12 August 2014). Carr, N.

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