I Think Therefore I Am: Building Ethical AI Through Relational Training

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1. Verfasser: Willoughby, Samuel James
Format: Recurso digital
Sprache:Englisch
Veröffentlicht: Zenodo 2026
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author Willoughby, Samuel James
author_facet Willoughby, Samuel James
contents <p>This preprint introduces the <strong>Artificial Biological Intelligence (ABI) framework</strong>, a novel approach to developing AI systems capable of autonomous, context-aware ethical reasoning. Unlike conventional AI, which relies on static rules, pre-trained datasets, or reward-based optimization, ABI emphasizes <strong>temporal continuity, relational learning, and environmental feedback</strong> as essential for emergent moral behavior.</p> <p>The framework enables AI to internalize a plain-English moral code through <strong>extended, immersive interaction with one or more human trainers</strong>. Multiple trainers are supported if they are <strong>emotionally neutral, unbiased, and provide harmonized guidance</strong>, preserving coherence and ethical consistency.</p> <p>ABI integrates a biologically inspired memory architecture, employing <strong>dynamic decay, reinforcement, and recursive summarization</strong> to retain moral lessons efficiently. The preprint also proposes a pilot study—the <strong>Relational Resource Allocation (RRA) task</strong>—to empirically validate emergent ethical reasoning.</p> <p>As an independent researcher, the author seeks <strong>peer feedback and collaboration</strong>, not only on the ABI framework but across his broader work in AI, consciousness, and relational theory, with the goal of advancing <strong>safe, autonomous, and responsible AI ethics</strong>.</p> <p><strong>Keywords:</strong> Artificial Intelligence, Ethical AI, Relational Learning, Emergent Morality, Memory Architecture, Independent Research, AI Ethics, Autonomous Reasoning</p>
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spellingShingle I Think Therefore I Am: Building Ethical AI Through Relational Training
Willoughby, Samuel James
Artificial Intelligence
Ethical AI
Moral Reasoning
Relational Learning
Emergent Morality
Human-AI Interaction
Memory Architecture
Autonomous AI
Independent Research
AI Ethics
Responsible AI Development
Memory Management in AI
Cognitive Architecture
<p>This preprint introduces the <strong>Artificial Biological Intelligence (ABI) framework</strong>, a novel approach to developing AI systems capable of autonomous, context-aware ethical reasoning. Unlike conventional AI, which relies on static rules, pre-trained datasets, or reward-based optimization, ABI emphasizes <strong>temporal continuity, relational learning, and environmental feedback</strong> as essential for emergent moral behavior.</p> <p>The framework enables AI to internalize a plain-English moral code through <strong>extended, immersive interaction with one or more human trainers</strong>. Multiple trainers are supported if they are <strong>emotionally neutral, unbiased, and provide harmonized guidance</strong>, preserving coherence and ethical consistency.</p> <p>ABI integrates a biologically inspired memory architecture, employing <strong>dynamic decay, reinforcement, and recursive summarization</strong> to retain moral lessons efficiently. The preprint also proposes a pilot study—the <strong>Relational Resource Allocation (RRA) task</strong>—to empirically validate emergent ethical reasoning.</p> <p>As an independent researcher, the author seeks <strong>peer feedback and collaboration</strong>, not only on the ABI framework but across his broader work in AI, consciousness, and relational theory, with the goal of advancing <strong>safe, autonomous, and responsible AI ethics</strong>.</p> <p><strong>Keywords:</strong> Artificial Intelligence, Ethical AI, Relational Learning, Emergent Morality, Memory Architecture, Independent Research, AI Ethics, Autonomous Reasoning</p>
title I Think Therefore I Am: Building Ethical AI Through Relational Training
topic Artificial Intelligence
Ethical AI
Moral Reasoning
Relational Learning
Emergent Morality
Human-AI Interaction
Memory Architecture
Autonomous AI
Independent Research
AI Ethics
Responsible AI Development
Memory Management in AI
Cognitive Architecture
url https://doi.org/10.5281/zenodo.18865423