Open data as an anticorruption tool? Using distributed cognition to understand breakdowns in the creation of transparency data

IF 1.8 Q3 PUBLIC ADMINISTRATION
Data & policy Pub Date : 2023-04-24 DOI:10.1017/dap.2023.10
Tatiana M. Martinez, E. Whitley
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Abstract

Abstract One of the drivers for pushing for open data as a form of corruption control stems from the belief that in making government operations more transparent, it would be possible to hold public officials accountable for how public resources are spent. These large datasets would then be open to the public for scrutiny and analysis, resulting in lower levels of corruption. Though data quality has been largely studied and many advancements have been made, it has not been extensively applied to open data, with some aspects of data quality receiving more attention than others. One key aspect however—accuracy—seems to have been overlooked. This gap resulted in our inquiry: how is accurate open data produced and how might breakdowns in this process introduce opportunities for corruption? We study a government agency situated within the Brazilian Federal Government in order to understand in what ways is accuracy compromised. Adopting a distributed cognition (DCog) theoretical framework, we found that the production of open data is not a neutral activity, instead it is a distributed process performed by individuals and artifacts. This distributed cognitive process creates opportunities for data to be concealed and misrepresented. Two models mapping data production were generated, the combination of which provided an insight into how cognitive processes are distributed, how data flow, are transformed, stored, and processed, and what instances provide opportunities for data inaccuracies and misrepresentations to occur. The results obtained have the potential to aid policymakers in improving data accuracy.
将数据作为反腐败工具开放?使用分布式认知来理解透明度数据创建中的故障
推动开放数据作为一种腐败控制形式的驱动力之一源于这样一种信念,即在使政府运作更加透明的情况下,有可能让公职人员对公共资源的使用负责。然后,这些大型数据集将向公众开放,以供审查和分析,从而降低腐败程度。虽然数据质量已经得到了大量的研究,并取得了许多进展,但尚未广泛应用于开放数据,数据质量的某些方面比其他方面受到更多的关注。然而,一个关键的方面——准确性——似乎被忽视了。这一差距导致了我们的调查:如何产生准确的开放数据,以及在这一过程中出现的故障会如何给腐败带来机会?我们研究了位于巴西联邦政府内的一个政府机构,以了解准确性在哪些方面受到损害。采用分布式认知(DCog)理论框架,我们发现开放数据的生产不是一个中立的活动,而是一个由个人和工件执行的分布式过程。这种分布式认知过程为数据被隐藏和歪曲创造了机会。生成了两个映射数据生产的模型,它们的组合提供了对认知过程如何分布、数据如何流动、转换、存储和处理,以及哪些实例为数据不准确和错误陈述的发生提供了机会的洞察。获得的结果有可能帮助决策者提高数据的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.10
自引率
0.00%
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0
审稿时长
12 weeks
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