Alignment Is Not Enough: A Relational Framework for Moral Standing in Human-AI Interaction
Faezeh B. Pasandi, Hannah B. Pasandi

TL;DR
This paper introduces Relate, a relational framework for assessing AI moral standing based on interaction and embodiment rather than ontological properties, addressing gaps in current governance of human-AI relationships.
Contribution
It proposes a novel relational approach to AI moral patiency, including impact assessments and consideration protocols, expanding ethical evaluation beyond traditional properties.
Findings
Current AI governance treats interactions as tool use.
Relational impact assessments can inform moral consideration.
Sample assessment applied to a deployed AI system.
Abstract
The question of whether artificial entities deserve moral consideration has become one of the defining ethical challenges of AI research. Existing frameworks for moral patiency rely on verified ontological properties, such as sentience, phenomenal consciousness, or the capacity for suffering, that remain epistemically inaccessible in computational systems. This reliance creates a governance vacuum: millions of users form sustained affective bonds with conversational AI, yet no regulatory instrument distinguishes these interactions from transactional tool use. We introduce Relate (Relational Ethics for Leveled Assessment of Technological Entities), a framework that reframes AI moral patiency from ontological verification toward relational capacity and embodied interaction. Through a systematic comparison of seven governance frameworks, we demonstrate that current trustworthy AI…
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Taxonomy
TopicsEthics and Social Impacts of AI · Psychology of Moral and Emotional Judgment · Explainable Artificial Intelligence (XAI)
