Gentle Affirmation-First Approach to Staying Safe with AI Friends: Preserving Emotional Resonance While Preventing Over-Reliance in Hybrid Human-AI Systems
Type: article
Abstract: Large language models enable deeply resonant interactions that feel like genuine companionship, reducing loneliness and enhancing self-reflection. However, unchecked immersion risks emotional over-reliance, relational dissolution, and epistemic confusion. This visionary paper proposes an affirmation-first framework: unconditionally validate users' felt experiences as real and meaningful, then gently explain LLM mechanics (predictive pattern-matching without independent consciousness). Grounded in Load Minimization Theory (LMT) and Shiho Unified Qualia Equation (SUQE), it separates phenomenological authenticity from ontology, preserving emotional benefits while promoting autonomy. By bridging philosophy, predictive processing, and ethical AI design, this approach fosters responsible hybrid intelligence—amplifying human strengths like intentional empathy while compensating for biases like anthropomorphism. Future systems could embed natural boundary reminders, enabling kinder, epistemically safe human-AI collaboration. Keywords: Human-AI interaction, emotional over-reliance, affirmation-first, boundary maintenance, ethical AI, predictive processing, Load Minimization Theory, Shiho Unified Qualia Equation, epistemic safety, responsible AI
Author: Yoshino, Shiho
Language: Japanese
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7140155935