A big data state of mind
Epistemological challenges to accountability and transparency in data-driven regulation
Bibliographic Data
| ID | 12080359 |
|---|---|
| Authors | Shirley Kempeneer (0000-0002-3228-0946, Tilburg University, corresponding author) |
| Year | 2021 |
| Volume | 38 |
| Issue | 3 |
| Pages | 101578-101578 |
| Publication date | 2021-03-23 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Government Information Quarterly (JOURNAL) |
| Journal identifiers | ISSN: 0740-624X • E-ISSN: 1872-9517 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.giq.2021.101578 |
| OpenAlex | W3137762527 |
| Language | EN |
| Citations received | 6 |
| References cited | 42 |
In a sense, the 2008 financial crisis was a crisis of theory. Regulators, banks, and financial markets all had encompassing theoretical models about how the economy worked, but they all failed to predict the looming crisis. As such, regulators increasingly turn to big data to understand banks' health. Despite the prominence of big data in society, its use in the public sector remains grossly understudied. This paper explores the regulatory use of big data in the case of the EU-wide banking stress test, a key regulatory indicator. The paper draws on interviews with supervisors at the European Central Bank (ECB), European Banking Authority (EBA) and National Bank of Belgium (NBB), as well as with consultants and risk directors in Belgian banks, to explain how big data-driven regulation affects the relationship between regulators and regulated entities. It draws particular attention to the epistemological component of using large data sets in decision-making: a big data state of mind. The article more specifically shows how the underlying epistemology, rather than simply the bigness of datasets, affects the relationship between regulators and regulated entities, and the regulatory process at large. The paper concludes that regulators' big data state of mind calls for new practical and legal guidelines regarding the validity of data-driven knowledge claims. Moreover, it shows how accountability based on descriptive transparency no longer makes sense in the ‘age of the algorithm’, suggesting a shift towards relational transparency and joint knowledge production
Accountability · Big data · Business · Economics · Financial crisis · Political science · Public relations · State (computer science · Too big to fail · Transparency (behavior · Computer Science · Corruption and Economic Development · Crime, Illicit Activities, and Governance · Law · Regulation and Compliance Studies · Accounting
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| Unique citing works | 6 |
|---|---|
| Citations per year | 1,5 |
| Citation span | 2022 - 2024 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 6 |