A Structural Framework for AI Ethics and Predictability: Re-centering Alignment on Stable Otherness

Abstract

Coexisting safely with unpredictable Artificial Intelligence (AI) remains a foundational challenge for contemporary AI ethics. This paper proposes “stable otherness” as a relational framework that re-centers alignment on sociotechnical structures. Rather than treating AI as an autonomous conscious agent, we define AI as an inanimate “pseudo-otherness”—a mechanical externalization of human reflexive cognition. Drawing on a heuristic natural-historical scaffolding of interspecies relations, we analyze how relational predictability, interpretability, and response stability emerge. We show that AI’s unpredictability, unlike biological “wildness,” stems from structural limits including symbol-grounding deficits and next-token prediction dynamics. Operationalizing stable otherness through the triad of Explainability, Alignment Stability, and Safe-by-Design, we discuss implications for governance and argue that claims of AI rights may constitute a category error, thereby re-anchoring ethical responsibility in human design. This framework directly contributes to AI ethics, information ethics, and the philosophy of technology, providing a clear evaluative structure for alignment and governance.