The Double Contingency Problem: AI Recursion and the Limits of Interspecies Understanding

TL;DR

The paper argues that state-of-the-art bioacoustic AI, built on recursive architectures, may distort the recursive structures through which animal communication acquires meaning. It introduces the double contingency concept and Yuk Hui's cosmotechnics to frame AI and animal cognition as irreducibly different recursive systems, whose interaction requires diplomacy rather than extraction. It proposes inter-recursive interfaces and a meta-recursive monitor, illustrated by an elephant case study, to guide architecture, evaluation, and governance toward respectful, participatory cross-species engagement. If adopted, this approach could shift bioacoustic AI toward relational sustainability and ethical, long-term multispecies understanding rather than purely performance-centric metrics.

Abstract

Current bioacoustic AI systems achieve impressive cross-species performance by processing animal communication through transformer architectures, foundation model paradigms, and other computational approaches. However, these approaches overlook a fundamental question: what happens when one form of recursive cognition--AI systems with their attention mechanisms, iterative processing, and feedback loops--encounters the recursive communicative processes of other species? Drawing on philosopher Yuk Hui's work on recursivity and contingency, I argue that AI systems are not neutral pattern detectors but recursive cognitive agents whose own information processing may systematically obscure or distort other species' communicative structures. This creates a double contingency problem: each species' communication emerges through contingent ecological and evolutionary conditions, while AI systems process these signals through their own contingent architectural and training conditions. I propose that addressing this challenge requires reconceptualizing bioacoustic AI from universal pattern recognition toward diplomatic encounter between different forms of recursive cognition, with implications for model design, evaluation frameworks, and research methodologies.