Ambiguous AI: Lossy Compression, Model Collapse, and Intercultural Sensitivity

Abstract

This paper examines the intersections of artificial intelligence (AI), cultural representation, and intercultural sensitivity, critiquing the impact of lossy compression and model collapse on cultural expression in AI systems. The study introduces “intentional patiency” as an alternative framework, conceptualizing AI not merely as an active agent but as a cultural receptor, and in turn, producer. AI systems often rely on compression algorithms that strip away cultural complexities, rendering nuanced traditions as simplified, stereotypical artifacts. Drawing on frameworks from Ted Chiang’s metaphor of lossy compression to Wendy Chun’s critique of noise, the essay explores how AI’s data optimization selectively filters cultural diversity, perpetuating dominant narratives while silencing marginalized voices. Model collapse in generative systems demonstrates while also exacertabting the pitfalls of homogenous data to reduce cultural representations to monolithic “single stories,” echoing concerns raised by Chimamanda Ngozi Adichie. To address this issue, this essay advocates for an interculturally sensitive AI development paradigm, incorporating cultural expansion and ethical safeguards to mitigate cultural erasure. Through the concept of “ambiguous AI,” a paradigm rooted in probabilistic engagement and intentional patiency, the essay seeks to reframe AI’s role as a nuanced participant in cultural discourse, promoting culturally adaptive architectures.