Human-Centered AI
AI and Non-Western Art Worlds: Reimagining Critical AI Futures through Artistic Inquiry and Situated Dialogue
This paper examines the potential for localized adaptation, appropriation and re-imagination of Al for non-western cultural expression, using the Persian Gulf as a case. Using sociologist Howard Becker's concept of 'art worlds' as a situated lens to evaluate generative AI, we set up an eight week experimentation and dialogue between artists, art historians and curators. Our project reveals how local art worlds 1) can appropriate AI tools to address contextual and cultural needs; 2) develop "hacks" to adapt AI for culturally-specific capabilities; and 3) can be a site for imagining alternative techno- logical trajectories. We thus showcase the importance of expanding the scope of Al evaluations to include the social dynamics Al op- erates in and its contexts of use. We also reflect on the power that local communities may have to interrupt Al with more culturally- relevant orientations and to offer visions for redesigning AI for non-Western creativity.
Executive Impact: AI in the Human-Centered AI Sector
Our study empirically demonstrates how local communities are appropriating and adapting AI, showing that HCI and critical scholarship have demonstrated how technologies transform through user co-option, refusal and resistance. Our findings extend this scholarship by showing how local art worlds will not merely be passive recipients or users of this emerging technology, but active shapers of the possibilities of this technology while also be constrained by structural power dynamics.
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Faster Creative Iteration
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This section explores how local communities, particularly in non-Western art worlds, are actively appropriating and adapting AI technologies to suit their unique cultural contexts and needs. It highlights the dynamic interplay between global technology and local creative practices, moving beyond passive consumption to active shaping and re-imagination.
This section delves into the importance of socially-situated AI evaluations. By focusing on local art worlds as interconnected networks of artists, historians, and curators, we gain deeper insights into the sociopolitical powers that AI operates within, shapes, and is shaped by. This expands the scope of AI evaluations beyond mere technical benchmarks.
This section showcases the potential of locally situated, art-based inquiry to imagine and demand alternative technological trajectories for AI. It shifts the evaluative framework from retroactive assessments to proactive, locally generated visions for redesigning AI to be more culturally relevant and aligned with community aspirations, particularly from non-Western perspectives.
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of artists engaged in 'hacks' to adapt AI for culturally-specific capabilities.
Enterprise Process Flow
Artists Experiment with Gen AI
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Log Reflections & Challenges
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Commentators Assess & Critique
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Collective Dialogue & Envisioning
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Redesign AI Futures
| Challenge | Traditional AI Approach | Art World Adaptation |
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| Cultural Bias |
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| Language Barriers |
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| Stereotypical Outputs |
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Artist-3: Reparative Representation
Artist-3 extensively trained custom Stable Diffusion models using archives of paintings from Iran, photographs of women in 19th century Iran, and crowdsourced images from Instagram followers. This iterative process allowed him to break out of existing representational frames and move towards a more Iranian visual representation.
Key Takeaways: Customized datasets are crucial for culturally-situated AI. Iterative model chaining can produce nuanced, historically-informed outputs. Local communities can actively shape AI to tell their own stories.
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Your AI Transformation Roadmap
A phased approach to integrate culturally-sensitive AI, drawing lessons from pioneering research and practical applications.
Phase 1: Pilot & Localized Data Curation
Initiate pilot programs with local art institutions to curate culturally-specific datasets. Develop tools for community-led data annotation and contextualization.
Phase 2: Develop Custom AI Adapters
Engineer plug-and-play AI 'adapters' that allow artists to seamlessly integrate local datasets and steer models with culturally-situated constraints.
Phase 3: Integrate Ethical AI Education
Launch educational modules within creative platforms to foster critical thinking about AI's biases and historical contexts among artists and users.
Phase 4: Establish Decentralized Data Governance
Work with legal and tech experts to create decentralized data trusts, giving communities ownership and control over their cultural data used by AI.
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