Navigating Ethical Challenges in Cryptocurrency and Blockchain Technologies
Bibliographic record
Abstract
The emergence of blockchain and cryptocurrency technologies has transformed digital ecosystems, introducing opportunities for innovation and efficiency alongside profound ethical challenges. This paper explores key ethical considerations in cryptocurrency and blockchain, including the decentralization of financial systems, the balance between privacy and transparency, the use of blockchain for surveillance, and the socio-economic impacts on vulnerable populations. The authors delve into the contrasting emphasis on ethical considerations for financial solutions deployed in developed and developing countries. The borderless nature of blockchain and cryptocurrencies enables decentralised international transactions while simultaneously introducing specific challenges regarding the definition of applicable law and other jurisdictional legal matters. Through a combination of literature analysis and illustrative case studies, the authors examine the complex ethical dilemmas that accompany these technologies in combination with their actual and perceived links to crime. The findings aim to provide actionable insights for policymakers, industry leaders, and researchers, fostering the responsible and equitable adoption of blockchain and cryptocurrency technologies.
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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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".