Computing ethics: the case for codes of ethics and privacy policies
Bibliographic record
Abstract
Ethics and privacy are integral to life although limited research has been conducted relative to global codes of ethics and privacy policies of corporations. This piqued the interest on this research where the first contribution examines codes of ethics worldwide. It compares codes of different societies to IEEE and proposes changes which address issues of diversity, culture, and sociopolitical differences. Four countries have adopted the IEEE codes of ethics, while 28 countries have some variations. A global code of ethics would be useful in a world without borders. The second contribution introduces new guidelines for Canadian corporations regarding privacy policies. It examines the compatibility and compliance of corporate privacy policies with PIPEDA. An examination of the corporations revealed only 1,017 have public-facing privacy policies on their websites and some do not seem to satisfy all PIPEDA principles. New guidelines will help to ensure a better compliance with PIPEDA by corporations.
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 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 | 0.072 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.017 | 0.139 |
| Scholarly communication | 0.028 | 0.035 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".