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

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 more accountable. These large data sets would be open to the public for scrutiny, 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 (namely completeness and timeliness). 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, where acts of corruption were being committed, to understand in what ways is accuracy compromised. Adopting a Distributed Cognition (DCog) theoretical framework, we found that production of open data is not a neutral activity; instead it is a distributed process performed by individuals and artefacts, highlighting an important and ambiguous role for technology in the fight against corruption. This distributed cognitive process creates opportunities for data to be concealed and misrepresented. Through DCog, two models mapping data production were generated, the combination of which provided an insight into how cognitive processes are distributed, how data flows, is transformed, stored, and processed, and what instances provide opportunities for data inaccuracies and misrepresentations to occur.