Relational Effect Without Personhood: A Taxonomy of Presence-Effect, Correction Uptake, and Burden Location in Human-AI Interaction

Human-AI interaction can become socially consequential before there is good reason to attribute personhood, consciousness, or moral interiority to an AI system. Existing literatures on social presence, anthropomorphism, AI agency, moral status, and governance capture important parts of this middle zone, but they often leave felt relational effect, durable correction, and consequence-bearing structure on different analytical planes. This article proposes a taxonomy for classifying AI systems once relational effect is separated from personhood. The contribution is diagnostic rather than metaphysical. The paper distinguishes presence-effect from personhood, local accommodation from correction uptake, and output influence from burden-bearing structural agency. These distinctions yield four structural states: stochastic echo, reactive coupler, closed matrix, and candidate structural presence. The states are not a simple ladder of intelligence or consciousness. They describe different configurations of fluency, coupling, boundary, continuity, reality contact, correction uptake, and burden registration. Burden location serves as a governance test: when AI output steers action, who verifies it, who absorbs error, who can contest it, and what changes in the system after failure? The taxonomy predicts different risk profiles even when systems appear equally human-like. Companion systems may function as reactive couplers; rigid decision systems may function as closed matrices; fluent chat systems may remain stochastic echoes despite strong presence-effects. Candidate structural presence is reserved for future systems that show durable boundary, cross-context continuity, evidence-sensitive correction uptake, trace-bearing revision, and functional registration of consequence. The framework does not infer phenomenal consciousness. It offers a governance vocabulary for systems that may be socially powerful before they are persons.

Authors

Institutions

Publication Details

Journal
Knowledge Commons (Lakehead University)
Published
2026-08-21
DOI
https://doi.org/10.17613/kwv02-zmv27
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Relational Effect Without Personhood: A Taxonomy of Presence-Effect, Correction Uptake, and Burden Location in Human-AI Interaction

Vladisav Jovanovic
Knowledge Commons (Lakehead University)
Ethics and Social Impacts of AI
article

Relational Effect Without Personhood: A Taxonomy of Presence-Effect, Correction Uptake, and Burden Location in Human-AI Interaction

Vladisav Jovanovic
article en

Abstract

Human-AI interaction can become socially consequential before there is good reason to attribute personhood, consciousness, or moral interiority to an AI system. Existing literatures on social presence, anthropomorphism, AI agency, moral status, and governance capture important parts of this middle zone, but they often leave felt relational effect, durable correction, and consequence-bearing structure on different analytical planes. This article proposes a taxonomy for classifying AI systems once relational effect is separated from personhood. The contribution is diagnostic rather than metaphysical. The paper distinguishes presence-effect from personhood, local accommodation from correction uptake, and output influence from burden-bearing structural agency. These distinctions yield four structural states: stochastic echo, reactive coupler, closed matrix, and candidate structural presence. The states are not a simple ladder of intelligence or consciousness. They describe different configurations of fluency, coupling, boundary, continuity, reality contact, correction uptake, and burden registration. Burden location serves as a governance test: when AI output steers action, who verifies it, who absorbs error, who can contest it, and what changes in the system after failure? The taxonomy predicts different risk profiles even when systems appear equally human-like. Companion systems may function as reactive couplers; rigid decision systems may function as closed matrices; fluent chat systems may remain stochastic echoes despite strong presence-effects. Candidate structural presence is reserved for future systems that show durable boundary, cross-context continuity, evidence-sensitive correction uptake, trace-bearing revision, and functional registration of consequence. The framework does not infer phenomenal consciousness. It offers a governance vocabulary for systems that may be socially powerful before they are persons.

Knowledge Commons (Lakehead University)
Idaho Commission for Libraries (US), Israel Cancer Research Fund (US), ICF International (United States) (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 6%
Ethics and Social Impacts of AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.