The Ethical and Legal Dilemmas of Digital Accountability Research and the Utility of International Norm-Setting
Bibliographic record
Abstract
Nearly every aspect of our life is impacted by digital technologies manufactured and sold by companies. Legislative frameworks to limit the harms of such technologies have been slow to develop and remain entangled in controversy.1 The expanding role of digital technologies has been accompanied by a disturbing descent into authoritarianism in many countries that is also, in part, fueled by these very same tools.2 The decline of liberal democratic institutions is said to be linked to various properties of the digital ecosystem—from security flaws in popular applications used by states to engage in covert and remote surveillance3 to the development and exploitation of social media algorithms that push violent and divisive content.4 There is no doubt, then, that digital accountability research—which we define as evidence-based research seeking to track and expose risks to civil society in the digital ecosystem—is critical. This essay highlights the legal and ethical challenges faced in digital accountability research and concludes that a comprehensive and global ethical framework for such research is a critical step forward. As legal frameworks and norms continue to shift with respect to digital accountability research, such collaborative, international norm-setting would help ensure that digital accountability research continues.
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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 | 0.259 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.134 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.018 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 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".