Algorithms are starting to play an increasingly prominent role in government organizations. The argument is that algorithms can make more objective and efficient decisions than humans. At the same time, recent scandals have highlighted that there are still many problems connected to algorithms in the public sector. There is an increasing emphasis on ethical requirements for algorithms and we aim to connect these requirements to insights from public administration on the use of technologies in the public sector. We stress the need for responsible algorithmization – responsible organizational practices around the use of algorithms – and argue that this is needed to maintain the trust of citizens. We present two key components of responsible algorithmization – value-sensitivity and transparency – and show how these components connect to algorithmization and can contribute to citizen trust. We end the article with an agenda for research into the relation between responsible algorithmization and trust.

The police use all sorts of information to fulfil their tasks. Whereas collection and interpretation of information traditionally could only be done by humans, the emergence of ‘Big Data’ creates new opportunities and dilemmas. On the one hand, large amounts of data can be used to train algorithms. This allows them to ‘predict’ offenses such as bicycle theft, burglary, or even serious crimes such as murder and terrorist attacks. On the other hand, highly relevant questions on purpose, effectiveness, and legitimacy of the application of machine learning/‘artificial intelligence’ drown all too often in the ocean of Big Data. This is particularly problematic if such systems are used in the public sector in democracies, where the rule of law applies, and where accountability, as well as the possibility for judicial review, are guaranteed. In this article, we explore the role transparency could play in reconciling these opportunities and dilemmas. While some propose making the systems and data they use themselves transparent, we submit that an open and broad discussion on purpose and objectives should be held during the design process. This might be a more effective way of embedding ethical and legal principles in the technology, and of ensuring legitimacy during application.

This article focuses on the question how Big, Open and Linked Data systems (BOLD) are shifting human-data relations. BOLD is creating a new type of society which is both data-focused and data-driven. Both governments and citizens are measuring, analyzing and verifying data and acting upon these types of analyses. As BOLD is itself becoming intelligent, the process of collecting, linking, and analyzing data is no longer merely the domain of humans. Machine-learning is picking up speed and algorithmic accuracy is being maximized as data are becoming more complex and unpredictable its output. Both citizens and governments will increasingly have to deal with non-human actors in the form of intelligent data-driven systems. By referring to literature on human-animal relations this article makes the argument that data systems are gaining autonomy and a certain level of wildness. As such systems are mediating human relations, the article argues that social relations are shifting to becoming triad relationships in which intelligent information systems are a significant actor.