Algorithmic and data-driven systems are increasingly used in the public sector to improve the efficiency of existing services or to provide new services through the newfound capacity to process vast volumes of data. Unfortunately, certain instances also have negative consequences for citizens, in the form of discriminatory outcomes, arbitrary decisions, lack of recourse, and more. These have serious impacts on citizens ranging from material to psychological harms. These harms partly emerge from choices and interactions in the design process. Existing critical and reflective frameworks for technology design do not address several aspects that are important to the design of systems in the public sector, namely protection of citizens in the face of potential algorithmic harms, the design of institutions to ensure system safety, and an understanding of how power relations affect the design, development, and deployment of these systems. The goal of this workshop is to develop these three perspectives and take the next step towards reflective design processes within public organisations. The workshop will be divided into two parts. In the first half we will elaborate the conceptual foundations of these perspectives in a series of short talks. Workshop participants will learn new ways of protecting against algorithmic harms in sociotechnical systems through understanding what institutions can support system safety, and how power relations influence the design process. In the second half, participants will get a chance to apply these lenses by analysing a real world case, and reflect on the challenges in applying conceptual frameworks to practice.@en

Despite having set the theoretical ground for explainable systems decades ago, the information system scholars have given little attention to new developments in the decision-making with humans-in-the-loop in real-world problems. We take the sociotechnical system lenses and employ mixed-method analysis of a field intervention to study the machine-learning informed decision-making with interpreted models' outputs. Contrary to theory, our results suggest a small positive effect of explanations on confidence in the final decision, and a negligible effect on the decisions' quality. We uncover complex dynamic interactions between humans and algorithms, and the interplay of algorithmic aversion, trust, experts' heuristic, and changing uncertainty-resolving condititions.@en

The recent surge of interest in algorithmic decision-making among scholars across disciplines is associated with its potential to resolve the challenges common to administrative decision-making in the public sector, such as greater fairness and equal treatment of each individual, among others. However, algorithmic decision-making combined with human judgment may introduce new complexities with unclear consequences. This article offers evidence that contributes to the ongoing discussion about algorithmic decision-making and governance, contextualizing it within a public employment service. In particular, we discuss the use of a decision support system that employs an algorithm to assess individual risk of becoming long-term unemployed and that informs counselors to assign interventions accordingly. We study the human interaction with algorithms in this context using the lenses of human detachment from and attachment to decision-making. Employing a mixed-method research approach, we show the complexity of enacting the potentials of the data-driven decision-making in the context of a public agency.

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We partner with a leading European healthcare provider and design a mechanism to match patients with family doctors in primary care. We define the matchmaking process for several distinct use cases given different levels of available information about patients. Then, we adopt a hybrid recommender system to present each patient a list of family doctor recommendations. In particular, we model patient trust of family doctors using a large-scale dataset of consultation histories, while accounting for the temporal dynamics of their relationships. Our proposed approach shows higher predictive accuracy than both a heuristic baseline and a collaborative filtering approach, and the proposed trust measure further improves model performance.@en

We propose a collaborative filtering recommender system to match patients with doctors in primary care. In particular, we model patient trust in primary care doctors using a large-scale dataset of consultation histories, and account for the temporal dynamics of their relationships, defined in a novel quantitative measure of patient-doctor trust. Our proposed approach shows higher predictive accuracy than a heuristic baseline, as well as a collaborative filtering approach without the trust measures.@en