Machine learning (ML) algorithms have now entered public decision-making surrounded by enthusiasm, for the possible positive impact they may have on services and citizens. However, their introduction brings with it numerous problems that are left in the background or not even addressed. Academic contributions are growing, and often discuss general challenges, such as a lack of transparency, a lack of accountability and the issue of discrimination. However, the wickedness of public decision-making and specific public decision-making characteristics are not fully acknowledged in the literature, and the impacts of these characteristics are underexplored. With a focus on public decision-making and Llgorithms in the public sector, in this chapter, we provide a conceptual overview based on a narrative literature review. Specifically, the chapter first offers an overview of public sector decision-making characteristics. After describing our methodology, the study offers an overview of available studies focusing on decision-making with algorithms and decision-making about algorithms. Then, implications in light of specific public sector characteristics are discussed. The main implication is the amplification of existing challenges that exist with both public decision-making and ML algorithms. Finally, some conclusions are drawn.@en

Purpose: The purpose of this paper is to introduce the papers in this special issue on humans, algorithms and data. The authors first set themselves the task of identifying the main challenges arising from the adoption and use of algorithms and data analytics in management, accounting and organisations in general, many of which have been described in the literature. Design/methodology/approach: This paper builds on previous literature and case studies of the application of algorithm logic with artificial intelligence as an exemplar of this innovation. Furthermore, this paper is triangulated with the findings of the papers included in this special issue. Findings: Based on prior literature and the concepts set out in the papers published in this special issue, this paper proposes a conceptual framework that can be useful both in the analysis and ordering of the algorithm hype, as well as to identify future research avenues. Originality/value: The value of this framework, and that of the papers in this special issue, lies in its ability to shed new light on the (neglected) connections and relationships between algorithmic applications, such as artificial intelligence. The framework developed in this piece should stimulate scholars to explore the intersections between “technical” as well as organisational, social and individual issues that algorithms should help us tackle.@en

Big data promises to transform public decision-making for the better by making it more responsive to actual needs and policy effects. However, much recent work on big data in public decision-making assumes a rational view of decision-making, which has been much criticized in the public administration debate. In this paper, we apply this view, and a more political one, to the context of big data and offer a qualitative study. We question the impact of big data on decision-making, realizing that big data – including its new methods and functions – must inevitably encounter existing political and managerial institutions. By studying two illustrative cases of big data use processes, we explore how these two worlds meet. Specifically, we look at the interaction between data analysts and decision makers. In this we distinguish between a rational view and a political view, and between an information logic and a decision logic. We find that big data provides ample opportunities for both analysts and decision makers to do a better job, but this doesn't necessarily imply better decision-making, because big data also provides opportunities for actors to pursue their own interests. Big data enables both data analysts and decision makers to act as autonomous agents rather than as links in a functional chain. Therefore, big data's impact cannot be interpreted only in terms of its functional promise; it must also be acknowledged as a phenomenon set to impact our policymaking institutions, including their legitimacy.@en