Art, community and AI: images for an affective memory - Bañuelos Capistrán, Jacob; Zavala Scherer, Diego; Lugo Rodríguez, Nohemí (Frontiers in Communication, 2025)
This research examines the potential of artificial intelligence (AI) image generation technologies for humanistic applications, specifically focusing on the creation, reconstruction, and reinvention of nonexistent visual archives through which communities can restore affective and identity-based memories. The theoretical framework draws from multiple perspectives: affect theory of postmemory, techno-aesthetic and affective apparatus theory, the concept of insubordination of signs, distribution of the sensible, and theories of affective memory and visual archives. Through case study analysis, this investigation examines the creative, ethical, and theoretical-methodological strategies employed in four artistic and experimental projects, each driven by distinct aesthetic, emotional, political, activist, and vindicatory objectives. The research identifies three distinct levels of intervention in fractured memory through AI: the socio-political (evidenced in projects addressing historical invisibility and state violence), the community-cultural (manifested in projects recovering cultural traditions), and the therapeutic-personal (focused on individual memory restoration). The findings demonstrate the significant potential for expanding generative AI applications toward emotional repair, memory reinvention, and transformation of established sentiment structures, introducing the original concepts of “algorithmic postmemory” as a framework for understanding AI’s active role in memory construction and “affective symbolic documentalism” for comprehending the testimonial value of AI-generated imagery. These applications challenge dominant discourses that render specific social collectives invisible within society while offering new methodological approaches for the restoration of fractured memories and historical healing.