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

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...

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Veröffentlicht in:Big data & society Jg. 9; H. 1
Hauptverfasser: Dekker, Rianne, Koot, Paul, Birbil, S. Ilker, van Embden Andres, Mark
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London, England SAGE Publications 01.01.2022
Sage Publications Ltd
SAGE Publishing
Schlagworte:
Accountability
Algorithms
Bias
Big Data
Case studies
Co-design
Data processing
Governance
Public officials
Refugee camps
Refugees
Reinforcement
Scrutiny
Transparency
Algorithmic governance
co-design
refugee migration
big data
public service delivery
algorithm design
ISSN:2053-9517, 2053-9517
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Zusammenfassung: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.
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ISSN:2053-9517
2053-9517
DOI:10.1177/20539517221087855