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\u00a0rather than acknowledging collective contributions to artistic knowledge. In this paper, we counter these assumptions by theorizing \u2018collective agency\u2019 as a critical conceptual lens to rethink artists\u2019 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, cocreation, 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\u2019 solidarity, community-centric data stewardship, and collective bargaining mechanisms in ethical development of T2I AI tools to reclaim artists\u2019 control over their creative practices in the AI age.<\/p>\n","protected":false},"excerpt":{"rendered":"
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. 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