Kafka's Wallet: Building Trust Despite Financial Surveillance in CBDCs
Bibliographic record
Abstract
As central bank digital currencies (CBDC) are increasingly developed, piloted, and launched around the world, central bankers have begun to worry about consumer adoption.Privacy concerns remain a common critique of CBDCs and poses a serious challenge for adoption as consumers worry about third-party data brokers, data breaches and government surveillance.Therefore, we develop a framework, derived from surveillance studies, to conceptualize how central banks current conceptions of privacy facilitate unregulated consumer monitoring.We then evaluate three central bank's digital currency pilot programs, Canada, Japan, and Sweden, towards comparing their different approaches to privacy.We find that while all three central banks considered privacy to be a fundamental feature of CBDCs, they all fail to thoroughly address issues surrounding the processing and retention of customer data.Finally, we discuss the implications of our findings for gaining consumer trust in CBDCs.We propose a future research agenda for further CBDC development and argue that central banks need to balance national security and privacy.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".