The Impacts and Governance of Artificial Intelligence: A Transfeminist Perspective
Bibliographic record
Abstract
This thesis investigates the impacts and governance of artificial intelligence (AI) systems through a transfeminist lens, focusing analysis upon challenges of power, exclusion, and injustice alongside opportunities for advancing equity, community-based resistance, and transformative change. AI governance is a field of research and practice seeking to maximize benefits and minimize harms caused by AI systems. However, AI governance is frequently ineffective at preventing AI systems from causing harm to society and the environment, with historically marginalized groups being particularly vulnerable to harm. Applying a framework of theories drawn from service science, feminist studies, and trans studies, I analyze relationships between AI governance and harm prevention in three separate co-authored, peer-reviewed articles. The first article develops a theory linking beneficial and harmful impacts caused by AI systems to the value chains through which various actors integrate resources and co-create value throughout the AI system lifecycle. This theory is applied to an integrative review of ethical concerns implicated in AI systems and to discuss future directions for intervening in the impacts caused by AI systems. The second article presents a semi-systematic review and content analysis of 84 AI governance initiatives launched by federal and provincial governments in Canada from 2017 to 2022. AI governance initiatives are used to organize many types of interventions, and Canada’s initiatives favor intervention in the impacts of AI on Canadian industry, innovation, and technology production and adoption over more direct intervention in societal and environmental impact. The third article applies thematic analysis methods to data from interviews with 20 leaders of Canadian AI governance initiatives and subject matter experts. AI governance systems in Canada function at multiple levels of scale, and Canada’s national-scale AI governance system consists of a diverse range of actors, resources, networks, logics, functional bounds, rules, and perceptions of benefit and harm. The thesis concludes by synthesizing findings from the three articles into a set of reflections with a unifying theme: the effectiveness of AI governance at preventing harm is limited by power imbalances, and a transfeminist approach to AI governance can support future AI governance research, practices, and systems in addressing those limitations.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.086 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".