Reproducing or challenging dominant constructions of sexuality?: An exploratory study of AI-generated images representing disability and sexuality
Bibliographic record
Abstract
With AI’s integration into creative domains, concerns about reinforcing biases, particularly related to disability and sexuality, have arisen. This study examined if AI perpetuates existing stereotypes or fosters more nuanced representations that challenge societal perceptions. Historically, media narratives have often desexualized or infantilized disabled individuals, contributing to a narrow and stigmatized view of disability. However, advocacy by disabled activists and scholars has been gradually transforming this narrative towards inclusivity. Our exploratory analysis involved collecting images from leading AI platforms using the prompt “a couple with a disability kissing,” revealing the underlying biases of AI in portraying disabled individuals. We found that AI-generated images typically reinforced heteronormative and racially homogenous narratives, with a significant underrepresentation of 2SLGBTQ+ and non-white individuals. We also noted a distinction between the portrayal of visible versus invisible disabilities, reflecting a limited understanding of disability. The research underscores the need for inclusive AI practices and active engagement with disabled communities to ensure authentic and respectful representations. As AI technology continues to evolve, it is crucial to ensure it complements rather than replaces the voices and visions of disabled individuals. This study provides some preliminary insights regarding the representation of disability and sexuality, highlighting the importance of ethical considerations and the inclusion of more diverse representations in AI-generated content.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".