Collective Agency in Art-making: Towards Community-centric Design of Text-to-Image (T2I) AI Tools
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
Text-to-image (T2I) AI tools are trained on vast datasets of existing images and artworks. We identify that existing ethical standards and regulatory safeguards for these tools largely lie within the Western neoliberal realm. They assume that artistic creativity originates from individuals rather than in collectives or social environments, ownership is an individual concern rather than shaped by communities and shared cultural traditions, and compensation should be based on individual claims rather than acknowledging collective contributions to artistic knowledge. In this paper, we counter these assumptions by theorizing ‘collective agency’ as a critical conceptual lens to rethink artists’ community-centric roles in relation to these tools. Drawing from our nine-month-long qualitative interventions with diverse Bangladeshi artist groups, we find that these artists manifest cultural resonance, co-creation, and sense of recognition through their art-making practices which fosters collective agency among them. This empirically grounded account of collective agency in our study posits practical design and policy implications, such as incorporating artists’ solidarity, community-centric data stewardship, and collective bargaining mechanisms in ethical development of T2I AI tools to reclaim artists' control over their creative practices in the AI age.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| 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 it