Bibliographic record
Abstract
Tech companies bypass privacy laws daily, creating harm for profit. The information economy is plagued with hidden harms to people’s privacy, equality, finances, reputation, mental wellbeing, and even to democracy, produced by data breaches and data-fed business models. This book explores why this happens and proposes what to do about it. Legislators, policymakers, and judges are trapped into ineffective approaches to tackle digital harms because they work with tools unfit to deal with the unique challenges of data ecosystems that leverage AI. People are powerless towards inferences about them that they can’t anticipate, interfaces that manipulate them, and digital harms they can’t escape. Adopting a cross-jurisdictional scope, this book describes how laws and regulators can and should respond to these pervasive and expanding harms. In a world where data is everywhere, one of society’s most pressing challenges is addressing power discrepancies between the companies that profit from personal data and the people whose data produces profit. Doing so requires creating accountability for the consequences of corporate data practices—not the practices themselves. Laws can achieve this by creating a new type of liability that recognizes the social value of privacy, uncovering dynamics between individual and collective digital harms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".