Data breaches in the age of surveillance capitalism: Do disclosures have a new role to play?
Bibliographic record
Abstract
The rise of big data has led to profound changes to the dynamics of accumulation and profiteering. Today, data is captured, produced, and reproduced with such regularity that its collection, utility, and value can go largely unnoticed, giving rise to “surveillance capitalism” (Zuboff, 2019a). This paper explores emerging forms of exploitation within the data economy, including the rise of “instrumentarian power” (Zuboff, 2019a), opacity surrounding data collection and use, and the impact of data breaches on our capacity to function within the information economy. We consider whether new forms of extended responsibility reporting may help to disrupt the trajectory of surveillance capitalism and democratise participation in the digital economy (Crawford, 2021). We draw on the accounting literature on organisational disclosures to consider whether the disclosure of data breaches might enhance accountability by making aspects of the surveillance economy knowable to us. Empirically, our analysis considers the various rules currently governing the disclosure of data breaches in Australia, the US, the EU, and Canada, and the application of these rules in practice. While regulation of the digital economy is developing, laws governing the disclosure of data breaches are highly dependent on an organisation’s judgement. As a consequence, the nature, scale, and timeliness of these disclosures vary significantly, and the lack of clear routines makes it difficult for stakeholders to assess data risks. In response, we consider whether a mandatory disclosure framework might contribute usefully to the public “naming and taming” of surveillance capitalism (Zuboff, 2019a) and the democratisation of our digital future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.167 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.032 | 0.060 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".