The AI Anthropology Lifecycle (AAL) is an iterative process framework that organizes anthropological engagement with AI systems. It structures the work of AI Anthropology across two interconnected spaces: a Problem Space, where anthropologists study and research with AI, and a Solution Space, where they embed anthropological insight into AI design. The framework is not linear. Deployment creates new phenomena to study, feeding the cycle back to the beginning.
The three dimensions
The AAL organizes work across three dimensions. Anthropology of AI examines AI systems as culturally situated artifacts, studying how they are built, experienced, and contested. Anthropology by AI extends anthropological inquiry using AI-enabled computational methods, from Multi-Agent Ethnography to automated analysis and knowledge graph construction. These two dimensions constitute the Problem Space, generating the understanding that informs the third dimension. Anthropology for AI embeds that understanding into AI design, contributing cultural reasoning, contextual judgment, and ethical grounding to how systems are built. This is the Solution Space.
The bidirectional feedback loops between the Problem and Solution Spaces are what make the AAL iterative rather than sequential. Building an AI system (for) reveals new dynamics to study (of) and creates new opportunities for computational methods (by). Studying those dynamics in turn informs the next round of design. The framework treats this cycle as perpetual rather than as a project with a defined endpoint.
Origin
Matt Artz developed the AAL as the core process framework for AI Anthropology, publishing on it across multiple papers and chapters. The lifecycle informs the structure of his PhD dissertation at Aalborg University’s Techno-Anthropology Lab and provides the organizing logic for applied work including the AI Anthropology Toolkit.