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Algorithmic Transparency as a Challenge and Opportunity for Governance: The Case of the National Government of Finland

Governance
Government
Public Policy
Technology
Pertti Ahonen
University of Helsinki
Pertti Ahonen
University of Helsinki
Tero Erkkilä
University of Helsinki

Abstract

The purpose of the paper is to examine algorithmic transparency both as a challenge and an opportunity for governance. As a challenge, algorithmic transparency calls, for instance, ensuring the sufficient intelligibility of algorithms used in public governance for those concerned, such as citizens and voluntary organizations, small and larger firms, and lower including self-governing levels of government. It is also challenging to navigate between requirements of transparency and openness in a legal state (Rechtstaat) and an evolved democracy on the one hand, and on the other the strong legal protection of the intellectual capital of producers and owners of algorithms. As an opportunity, we explore the extent to which better algorithmic transparency may improve legal and democratic transparency and openness in general and create markets for domestic companies to offer tailor-made transparent algorithms but also with global market potential. Empirically, our paper explores experiences and policy and legislative intentions arisen in and around the national government of Finland concerning algorithmic transparency with special reference to public governance. We will briefly outline the development and the current use of algorithmic governance in Finland. We will also tackle systemic, structural and institutional issues related to algorithmic transparency, and attempts to regulate these issues. Our research material includes policy and other documentation and semi-structured interview material collected, for instance, from experts of the Ministry of Justice, Ministry of Finance, legal overseers, but also from other important stakeholders. In its empirical methodology our paper comprises a one-country case study with several points of observation, including comparisons between them.