Algorithm Registers: A Box-Ticking Exercise or Meaningful Tool for Transparency?

Governments increasingly implement algorithm registers—openly accessible overviews of algorithms used in decision-making—to promote transparency and accountability. However, the contribution of these registers is questioned: some scholars claim that governments only disclose politically non-sensitive algorithms and provide information with limited usefulness for accountability purposes. In contrast, a more optimistic view is also put forward: registers help organizations to be transparent to the public, which increases public trust in algorithm use. In response to this academic debate, this paper aims to provide a theoretical understanding and an empirical mapping of the different factors that shape algorithm registers and their implications. I conducted in-depth interviews with 27 respondents, both developers (public organizations) and key users (oversight authorities and societal watchdogs) of algorithm registers in the Netherlands, and I analyzed 33 policy documents. The findings nuance the current criticisms. While I find that public organizations indeed selectively disclose information and registers are currently not found useful by societal watchdogs and oversight authorities, there are also dynamics that could contribute to more responsible use of algorithms by public organizations, such as a potential disciplining effect of registers. This study highlights the importance of going beyond the rhetoric on algorithm registers, by establishing a deep understanding of the indirect and unexpected dynamics they create. The paper concludes that algorithm registers are currently not a meaningful tool for transparency, but a meaningful box-ticking exercise for public organizations.

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Publication Details

Journal
Information Polity
Published
2024-11-01
DOI
https://doi.org/10.1177/15701255241297107
Citations
4
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
2.10

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article

Algorithm Registers: A Box-Ticking Exercise or Meaningful Tool for Transparency?

Esther Nieuwenhuizen
4 citations
Information Polity
Ethics and Social Impacts of AI
2.10
article

Algorithm Registers: A Box-Ticking Exercise or Meaningful Tool for Transparency?

Esther Nieuwenhuizen
article en
4 citations

Abstract

Governments increasingly implement algorithm registers—openly accessible overviews of algorithms used in decision-making—to promote transparency and accountability. However, the contribution of these registers is questioned: some scholars claim that governments only disclose politically non-sensitive algorithms and provide information with limited usefulness for accountability purposes. In contrast, a more optimistic view is also put forward: registers help organizations to be transparent to the public, which increases public trust in algorithm use. In response to this academic debate, this paper aims to provide a theoretical understanding and an empirical mapping of the different factors that shape algorithm registers and their implications. I conducted in-depth interviews with 27 respondents, both developers (public organizations) and key users (oversight authorities and societal watchdogs) of algorithm registers in the Netherlands, and I analyzed 33 policy documents. The findings nuance the current criticisms. While I find that public organizations indeed selectively disclose information and registers are currently not found useful by societal watchdogs and oversight authorities, there are also dynamics that could contribute to more responsible use of algorithms by public organizations, such as a potential disciplining effect of registers. This study highlights the importance of going beyond the rhetoric on algorithm registers, by establishing a deep understanding of the indirect and unexpected dynamics they create. The paper concludes that algorithm registers are currently not a meaningful tool for transparency, but a meaningful box-ticking exercise for public organizations.

Information PolityVol. 29(4)
Utrecht University (NL)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Openalex Percentile: Top 10%
Ethics and Social Impacts of AI
2.10
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