The Ethics of BI with Private and Public Entities
Bibliographic record
Abstract
The Internet plays a vital role in data collection, information creation, and business intelligence (BI). The nature of information collected on the Internet, and the degree to which such information is collected, both have ethical ramifications. What data can be collected is very different from what data should be collected. Disregarding the latter question can be more profitable, but doing so can often involve unethical practices and more importantly, compromise the privacy of individuals. It has become widely known that private enterprises collect all manner of (BI) data about individuals, causing ethical concerns. The ethics of privacy do not affect private enterprises alone. In recent times the development and implementation of public information systems by public agencies have also resulted privacy breaches, both overt and inadvertent. This is despite the fact that governments have a responsibility to protect private data from external parties. While some privacy laws have been enacted, paradoxically, other governmental legislation such as the Freedom of Information Act (FOIA) has actually eased restrictions on the very information that the privacy laws have sought to protect. In this context, it is useful to compare US privacy regulations other countries, e.g. Canada. It is also useful to contrast federal regulations with those in States, e.g. Connecticut. Ethical concerns regarding private information have also spawned various “solutions” whose motives and success can be widely interpreted. It can be argued that the protection of privacy and private information are the responsibility of both private and public entities, who should take concrete steps to classify and protect private information
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".