Governance of algorithms: options and limitations

Abstract

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.

The Economics of Artificial Intelligence: A Survey

“…Nevertheless, computer selection biases might be present, leading to a whole new range of policy concerns. Algorithmic biases may be of different types (Saurwein et al, 2015), and occur for two main reasons: first, they make prediction based on data which is endogenously generated; second, they incorporate the behavioral biases of human beings. Recently, a heated debate arose regarding the use of algorithms for predicting recidivism in courtrooms.…”

The Power of Default on Reddit: A General Model to Measure the Influence of Information Intermediaries

“…The interested reader may refer to Just and Latzer (2017), Helberger et al (2015) as well as Barzilai-Nahon (2008) for systematic reviews of the normative backgrounds of bias in information intermediaries as well as the policy implications. Van Hoboken (2012) and Goldman (2006) offer some additional insight into the legal debate surrounding search engine bias, while Saurwein et al (2015) reflect on regulative approaches with respect to algorithms.…”

Internet governance as joint effort: (Re)ordering search engines at the intersection of global and local cultures

“…Drawing on this body of work, in this paper I investigate internet governance in practice by specifically focusing on search engines. Musiani (2013a) and Saurwein et al (2015) distinguish between two types of governance in regard to search engines and ranking systems: "governance by algorithms" and the "governance of algorithms". The first type relates to the governing power of algorithms themselves or the "power through the algorithm" (Beer 2009).…”