Shifting the Human-AI Relationship: Toward a Dynamic Relational Learning-Partner Model

• URL: http://arxiv.org/abs/2410.11864v1
• Date: Mon, 07 Oct 2024 19:19:39 GMT
• Title: Shifting the Human-AI Relationship: Toward a Dynamic Relational Learning-Partner Model
• Authors: Julia Mossbridge,
• Abstract summary: We advocate for a shift toward viewing AI as a learning partner, akin to a student who learns from interactions with humans. We suggest that a "third mind" emerges through collaborative human-AI relationships.
• Score: 0.0
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• Abstract: As artificial intelligence (AI) continues to evolve, the current paradigm of treating AI as a passive tool no longer suffices. As a human-AI team, we together advocate for a shift toward viewing AI as a learning partner, akin to a student who learns from interactions with humans. Drawing from interdisciplinary concepts such as ecorithms, order from chaos, and cooperation, we explore how AI can evolve and adapt in unpredictable environments. Arising from these brief explorations, we present two key recommendations: (1) foster ethical, cooperative treatment of AI to benefit both humans and AI, and (2) leverage the inherent heterogeneity between human and AI minds to create a synergistic hybrid intelligence. By reframing AI as a dynamic partner, a model emerges in which AI systems develop alongside humans, learning from human interactions and feedback loops including reflections on team conversations. Drawing from a transpersonal and interdependent approach to consciousness, we suggest that a "third mind" emerges through collaborative human-AI relationships. Through design interventions such as interactive learning and conversational debriefing and foundational interventions allowing AI to model multiple types of minds, we hope to provide a path toward more adaptive, ethical, and emotionally healthy human-AI relationships. We believe this dynamic relational learning-partner (DRLP) model for human-AI teaming, if enacted carefully, will improve our capacity to address powerful solutions to seemingly intractable problems.

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