Response by the Montreal AI Ethics Institute to the Santa Clara Principles on Transparency and Accountability in Online Content Moderation
Bibliographic record
Abstract
In April 2020, the Electronic Frontier Foundation (EFF) publicly called for comments on expanding and improving the Santa Clara Principles on Transparency and Accountability (SCP), originally published in May 2018. The Montreal AI Ethics Institute (MAIEI) responded to this call by drafting a set of recommendations based on insights and analysis by the MAIEI staff and supplemented by workshop contributions from the AI Ethics community convened during two online public consultation workshops. In its submission, MAIEI provides 12 overarching recommendations for the SCP, these include: 1) ensure there is more diversity in the content moderation process; 2) increase transparency into how platforms guide content-ranking; 3) disclose anonymized data on the training and/or cultural background of the content moderators for a platform; 4) tailor content moderation tools for specific issues; 5) draft specific guidelines for messaging applications with regards to data protection in content moderation; 6) take into account cultural differences relevant to what constitutes acceptable behavior online; 7) ensure platforms are transparent in regards to political advertising; 8) ensure greater transparency into the user-generated flagging/reporting systems deployed by a platform; 9) clarify if user content is flagged or reported through an automated system; 10) provide more data on the types of content removed from platforms; 11) provide clear guidelines on the appeal process, as well as data on prior appeals; 12) create a system for periodically revisiting the SCP so it reflects various technological advancements, modifications in law and policy, as well as changing trends or movements in content moderation.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".