'IF WE LOOK AT IT FROM AN LGBT POINT OF VIEW…’ MOBILIZING LGBTQ+ STAKEHOLDERS TO QUEER ALGORITHMIC IMAGINARIES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the results of an exploratory study that examines the social implications that platform algorithms raise for LGBTQ+ communities. We share the preliminary results of our Phase 2 group interviews, which were conducted with Canadian social media managers of LGBTQ+ non-profit organizations and with Canada-based LGBTQ+ tech workers. Algorithmic controversies relating to LGBTQ+ communities identified in Phase 1 were used as prompts to elicit discussions among participants. In this paper, we pay close attention to how participants queered dominant algorithmic imaginaries. Our preliminary analysis highlights four main findings. First, participants questioned dominant discourses that depict AI technology as being inherently new, instead re-inscribing algorithmic controversies within a long-lasting history of gender and sexual oppression. Second, participants reconfigured the ideal-type user embedded in sociotechnical systems but also identified challenges with effecting sociotechnical change as LGBTQ+ stakeholders. Third, participants subverted the notion of algorithmic resistance by questioning whether effective technological resistance should rely on technological misuse or disuse. Fourth, participants translated algorithmic controversies via their positionality as LGBTQ+ stakeholders to move beyond purely technicist considerations. Finally, we highlight the importance of mobilizing stakeholders from marginalized communities to contest the dominant discourses through which society makes sense of AI technologies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it