Bibliographic record
Abstract
In 2017, the Canadian federal government launched the “Pan-Canadian Strategy on Artificial Intelligence,” an ambitious plan to make Canada “a global leader in AI.” As part of this plan, the government sought to stimulate discussion about the ethical and societal implications of AI by sponsoring a series of AI & Society workshops. Hosted by the Canadian Institute for Advanced Research (CIFAR), these workshops brought together academics, engineers, and policymakers to discuss the impact of AI on healthcare, education, the modern workplace, Indigenous communities, and other areas. In its reports, CIFAR describes the AI & Society workshops as inclusive, diverse forums that allow actors from a range of different disciplinary, occupational, and ethnic backgrounds to express their opinions and concerns about AI. This paper investigates whether the AI & Society workshops are truly inclusive, or whether they privilege the voices and perspectives of some actors over others. It will be argued that, by inviting only “experts,” “thought leaders,” and “community leaders” to participate, the workshops systematically exclude laypeople and average consumers of technology. This is highly problematic since average consumers bear many of the social costs of advancements in AI. After critiquing the workshops, the paper proposes ways to amplify the voices of regular users of AI in public and intellectual discourse.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".