Speculative Fabulation and Algorithmic Resistance in Queer and Latinx Community-based AI Workshops

• In Event: Unsilencing the Past, Regifting the Present, and Manifesting Unforgettable Futures: Multiliteracies, Entertainment, and Popular Culture

Abstract

Objectives or purposes: This paper presents findings from a series of intergenerational, community-based workshops designed to support critical engagement with artificial intelligence technologies through speculative fabulation. Grounded in Latinx and queer cultural practices, the study explores how participants repurpose and “drag” AI tools to critique and reimagine sociotechnical systems. Participants included queer and BIPOC youth and adults engaged in creative and collaborative activities that involved facial recognition technologies (FRTs), generative AI, and digital storytelling. This project aims to examine how queer and BIPOC communities engage with and critique algorithmic systems through creative, culturally rooted practices. It seeks to understand how speculative fabulation can foster algorithmic literacies and support the design of more just and affirming technologies.

Perspective(s) or theoretical framework:
The project draws on speculative fabulation (Haraway, 2014; Author, 2023), Queer of color critique (Muñoz, 2009), and sociotechnical resistance (Benjamin, 2019; Noble, 2018). It also builds on learning sciences approaches to making and tinkering (Gutiérrez, 2008; Vossoughi et al., 2020), framing learning as emergent, relational, and rooted in cultural and historical contexts.

Methods, techniques, or modes of inquiry:
The study employs a design-based research approach, combining multimodal discourse analysis (Hull & Nelson, 2005) with inductive and deductive coding (Bogdan & Biklen, 1997; Saldaña, 2003). Over 9 hours of video and audio data from online workshops were coded for stances related to sociotechnical awareness, affect, and resistance. Digital artifacts were analyzed for visual and discursive markers of critique and fabulation.

Data sources, evidence, objects, or materials:
Data were collected from three DETER (Dragging Everyday Technologies Toward Equitable Realities) workshops involving 28 participants. Materials included 9 hours of Zoom recordings, transcripts, and 36 digital artifacts generated through AI image tools like Midjourney. Participant reflections, interactions, and group discussions served as key evidence.

Results and/or substantiated conclusions or warrants for arguments/point of view:
Participants demonstrated sociotechnical consciousness by identifying and articulating algorithmic bias. They creatively “hacked” tools to resist misclassification and reclaim representation. Importantly, participants did not stop at critique but imagined speculative futures in which AI tools affirm queer, Black, and Brown identities. These practices reflect algorithmic folklore (Savolainen, 2022) and highlight the generative potential of speculative, culturally sustaining inquiry.

Scientific or scholarly significance of the study or work:
This study contributes to emerging scholarship on critical AI literacies and the design of equitable technology education. It expands our understanding of how non-dominant communities engage with and resist sociotechnical systems, offering insights for researchers and educators developing culturally sustaining, justice-oriented pedagogies in an age of ubiquitous AI.

Author

• José Ramón Lizárraga, University of Colorado - Boulder