Montreal Declaration for Responsible AI: 10 Principles and 59 Recommendations
Bibliographic record
Abstract
The “Montreal Forum on the Socially Responsible Development of Artificial Intelligence” was a conference that began in November 2017, where more than 400 participants from various sectors and disciplines discussed the ethical and social implications of AI. The conference also led to the creation of the “Montreal Declaration for the Responsible Development of Artificial Intelligence” which was released in late 2018 with more than 500 signatories. The declaration outlines 10 principles and 59 recommendations to guide the development of AI in a way that respects human dignity, autonomy, justice, and democracy. Montreal's AI ethics principles have also been criticized. For example, it is argued that it does not cover the potential malicious use of AI for activities such as warfare, surveillance, or personalized propaganda, and does not offer specific guidance or mechanisms for its application and enforcement. Either way, it is considered an important step in the development of AI ethics and has been widely recognized for its global and integrative approach, and as a reference point for subsequent efforts.
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 | 0.060 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.026 | 0.011 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.037 | 0.039 |
| Insufficient payload (model declined to judge) | 0.034 | 0.032 |
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".