Co-designing algorithms for governance: Ensuring responsible and accountable algorithmic management of refugee camp supplies

Abstract

There is increasing criticism on the use of big data and algorithms in public governance. Studies revealed that algorithms may reinforce existing biases and defy scrutiny by public officials using them and citizens subject to algorithmic decisions and services. In response, scholars have called for more algorithmic transparency and regulation. These are useful, but ex post solutions in which the development of algorithms remains a rather autonomous process. This paper argues that co-design of algorithms with relevant stakeholders from government and society is another means to achieve responsible and accountable algorithms that is largely overlooked in the literature. We present a case study of the development of an algorithmic tool to estimate the populations of refugee camps to manage the delivery of emergency supplies. This case study demonstrates how in different stages of development of the tool—data selection and pre-processing, training of the algorithm and post-processing and adoption—inclusion of knowledge from the field led to changes to the algorithm. Co-design supported responsibility of the algorithm in the selection of big data sources and in preventing reinforcement of biases. It contributed to accountability of the algorithm by making the estimations transparent and explicable to its users. They were able to use the tool for fitting purposes and used their discretion in the interpretation of the results. It is yet unclear whether this eventually led to better servicing of refugee camps.

There is an elephant in the room: towards a critique on the use of fairness in biometrics

“…However, in this paper, we argue that part of the literature of fairness in biometrics neglects how these systems are implemented in real life for migration control. We also observe this trend beyond biometrics, proposing approaches to better distribute asylum seekers within a country [4,11,25,57]. However, algorithmic fairness cannot distribute justice in scenarios which intended purpose is to discriminate and that consistently jeopardise fundamental rights.…”

Blinded by “algo economicus”: Reflecting on the assumptions of algorithmic management research to move forward

“…Further, Seaver (2017) also highlights the ethnographic method as a way of overcoming the black box idea but moreover presents critical ideas on the overestimation of “algorithms” in algorithmic studies and offers strategies and ideas on how to access contexts with algorithmic systems in place. A last example is given by Dekker et al (2022) in the context of managing refugee camps, where their study on co‐designing algorithms shows how human agency, including knowledge from the field, shaped the algorithm. The study simultaneously emphasizes the importance of data throughout the design of algorithms.…”

Taylorism on steroids or enabling autonomy? A systematic review of algorithmic management

“…However, since then, scholars have widened their scope beyond platform companies, including more diverse contexts in the empirical record. For example, studies have focused on how digital control methods impact credit agents' labour process in a lending company (Terry et al 2021), how algorithms can be used to create diverse teams within an organization(Gómez-Zará et al 2022) and how co-designed algorithmic systems can be used in responsible governing of a refugee camp (Dekker et al 2022). It should also be noted that it is entirely possible that scholars have not used the term algorithmic management when studying digital management technologies in traditional office settings.…”