Aligning to a Moving Target: When AI Gazes into the Human, the Human Also Adjusts into AI

When an AI is fitted to a human, interaction with that AI can change the human’s preferences, capacities, and expectations, and the human to whom alignment refers is then no longer fixed. This paper asks under what conditions judging alignment successful by reference to a changed human is legitimate. Part 1 sets out a three-stage structure, names the phenomenon in which the criterion of success moves inside the interaction Endogenous Alignment Criterion Shift, and proposes four diagnostic items that separate recursion from harm to safety: the independence and legitimate updating of the reference criterion, the effectiveness of independent verification, the substantive preservation of options, and the possibility of exit. Part 2 draws on a Japanese national survey (N = 11,939), examining how the association between emotional dependence on AI and loneliness varies by social support. Contrary to the prediction, this association was stronger among respondents with more support. Some who identified an AI as a best friend or partner did not affirm that any current AI was conscious. Part 3 places legitimacy in the process of updating, specifies five review conditions, reads the diagnostic items as design requirements for coexistence environments, and proposes a longitudinal design.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-11
DOI
https://doi.org/10.5281/zenodo.24142247
Primary Topic
Ethics and Social Impacts of AI
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
preprint

Aligning to a Moving Target: When AI Gazes into the Human, the Human Also Adjusts into AI

Haruka Shibata, Kenji Yamada
Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
preprint

Aligning to a Moving Target: When AI Gazes into the Human, the Human Also Adjusts into AI

Haruka Shibata, Kenji Yamada
preprint en

Abstract

When an AI is fitted to a human, interaction with that AI can change the human’s preferences, capacities, and expectations, and the human to whom alignment refers is then no longer fixed. This paper asks under what conditions judging alignment successful by reference to a changed human is legitimate. Part 1 sets out a three-stage structure, names the phenomenon in which the criterion of success moves inside the interaction Endogenous Alignment Criterion Shift, and proposes four diagnostic items that separate recursion from harm to safety: the independence and legitimate updating of the reference criterion, the effectiveness of independent verification, the substantive preservation of options, and the possibility of exit. Part 2 draws on a Japanese national survey (N = 11,939), examining how the association between emotional dependence on AI and loneliness varies by social support. Contrary to the prediction, this association was stronger among respondents with more support. Some who identified an AI as a best friend or partner did not affirm that any current AI was conscious. Part 3 places legitimacy in the process of updating, specifies five review conditions, reads the diagnostic items as design requirements for coexistence environments, and proposes a longitudinal design.

Zenodo (CERN European Organization for Nuclear Research)
Kyoto University (JP)
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.

Laika access coming soon