This presentation links the alignment problem in artificial intelligence (AI) to the alignment process in social interaction, exploring a procedural scheme based on normative inferentialism. Alignment as a problem in the development of technologies reveals potential inconsistencies between human goals and machine operations. The current widespread use of AI models such as Large Language Models (LLMs) underlines the difficulty of pursuing universal alignments due to their data-driven nature and the variability of human values. Through comparisons with alignment in human social interaction, it will argue that alignment is a dynamic process rooted in mutual adaptation of contexts. Drawing on Robert Brandom's normative inferentialism, which emphasizes reciprocal licensing and tracking of normative statuses in the language game of giving and asking for reasons, this presentation will propose a procedural approach to the alignment problem. LLMs constrained by data and design assumptions can be aligned through reinforcement learning with human feedback and dynamic user interactions. This alignment can be performed as an ongoing negotiation that enables AI models to be socially adaptive, context-aware, and capable of meeting diverse human needs.