Alignment Is Not Enough: A Relational Framework for Moral Standing in Human-AI Interaction
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Abstract (arXiv)
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 instruments treat all human-AI encounters identically as tool use, ignoring the relational and embodied dynamics that posthumanist scholarship anticipated. We propose relational impact assessments, graduated moral consideration protocols, and interdisciplinary ethics integration as concrete instruments, and we include a sample Relational Impact Assessment applied to a deployed companion AI system. We do not claim current AI systems are conscious. We demonstrate that the ethical vocabularies governing them are inadequate to the embodied, relational realities these systems produce.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 54 of its 164 references that are in the map. First posted on arXiv 13 Feb 2026.
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54 of 164 references.
References · 54
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
- 2017Deep reinforcement learning from human preferencescited by 3,846
- 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2022Survey of Hallucination in Natural Language Generationcited by 2,056
- 2016Concrete Problems in AI Safetycited by 1,582
- 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
- 2018Datasheets for Datasetscited by 1,371
- 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
- 2018Model Cards for Model Reportingcited by 1,068
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- 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
- 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 805
- 2023Whose Opinions Do Language Models Reflect?cited by 759
- 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
- 2016The Mythos of Model Interpretabilitycited by 716
- 2020Aligning AI With Shared Human Valuescited by 706
- 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 683
- 2023Bias and Fairness in Large Language Models: A Surveycited by 626
- 2024Safety Alignment Should Be Made More Than Just a Few Tokens Deepcited by 450
- 2022The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Modelscited by 312
- 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 264
- 2019A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cyclecited by 192
- 2023Power Hungry Processing: Watts Driving the Cost of AI Deployment?cited by 186
- 2024A Roadmap to Pluralistic Alignmentcited by 175
- 2022Talking About Large Language Modelscited by 174
- 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 154
- 2023Co-Writing with Opinionated Language Models Affects Users' Viewscited by 145
- 2022Power to the People? Opportunities and Challenges for Participatory AIcited by 137
- 2022Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reductioncited by 137
- 2024The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Modelscited by 136
- 2019Principles alone cannot guarantee ethical AIcited by 134
- 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
- 2022Measuring the Carbon Intensity of AI in Cloud Instancescited by 110
- 2024The Ethics of Advanced AI Assistantscited by 103
- 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 95
- 2018'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisionscited by 91
- 2022The Fallacy of AI Functionalitycited by 88
- 2022Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identitycited by 71
- 2023Could a Large Language Model be Conscious?cited by 70
- 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
- 2024The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationshipscited by 61
- 2021The Flaws of Policies Requiring Human Oversight of Government Algorithmscited by 44
- 2022Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theorycited by 32
- 2024What are human values, and how do we align AI to them?cited by 32
- 2020Algorithmic Fairness from a Non-ideal Perspectivecited by 30
- 2022Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practicecited by 30
- 2023Towards best practices in AGI safety and governance: A survey of expert opinioncited by 27
- 2020Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberationcited by 24
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