Report prepared by the Montreal AI Ethics Institute (MAIEI) on\n Publication Norms for Responsible AI
Bibliographic record
Abstract
The history of science and technology shows that seemingly innocuous\ndevelopments in scientific theories and research have enabled real-world\napplications with significant negative consequences for humanity. In order to\nensure that the science and technology of AI is developed in a humane manner,\nwe must develop research publication norms that are informed by our growing\nunderstanding of AI's potential threats and use cases. Unfortunately, it's\ndifficult to create a set of publication norms for responsible AI because the\nfield of AI is currently fragmented in terms of how this technology is\nresearched, developed, funded, etc. To examine this challenge and find\nsolutions, the Montreal AI Ethics Institute (MAIEI) co-hosted two public\nconsultations with the Partnership on AI in May 2020. These meetups examined\npotential publication norms for responsible AI, with the goal of creating a\nclear set of recommendations and ways forward for publishers.\n In its submission, MAIEI provides six initial recommendations, these include:\n1) create tools to navigate publication decisions, 2) offer a page number\nextension, 3) develop a network of peers, 4) require broad impact statements,\n5) require the publication of expected results, and 6) revamp the peer-review\nprocess. After considering potential concerns regarding these recommendations,\nincluding constraining innovation and creating a "black market" for AI\nresearch, MAIEI outlines three ways forward for publishers, these include: 1)\nstate clearly and consistently the need for established norms, 2) coordinate\nand build trust as a community, and 3) change the approach.\n
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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 | 0.068 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.032 | 0.011 |
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".