Art as an Algorithmic Virus: Unifying the Generative Crash and AI Value Convergence via Cognitive Affordances
Abstract
Generative AI inherently triggers a computational failure mode in human observers (a "generative crash") due to a lack of latent intentionality required for Inverse Reinforcement Learning (IRL) convergence. Artistic appreciation operates as the biological execution of this IRL process. To address the generative crash and broader AI alignment failures, I introduce the Ghost Scale (an HCI cognitive affordance for identifying intentionality) and propose Cooperative Inverse Reinforcement Learning (CIRL) to mimic biological value transmission. The Intent Extraction Limit is formalized to define the prior relationship. Applying this model addresses two major issues: generative AI's friction with the art community (via the Ghost Scale, a cognitive affordance and UX framework for signaling intentionality) and AI alignment [via a proposed shift from Reinforcement Learning from Human Feedback (RLHF) toward top-down value capture through Cooperative Inverse Reinforcement Learning (CIRL) informed by world models]. Six empirical hypotheses are proposed to test the framework.