CROSS-DOMAIN SYNTHESIS: AI Ethics & Neuroscience 1. CONCEPTUAL BRIDGING: - Both fields examine decision-making: AI through algorithmic logic, neuroscience through neural mechanisms - Shared concern with bias: algorithmic bias vs. cognitive bias - Common framework: reward systems (reinforcement learning / dopaminergic pathways) 2. PATTERN RECOGNITION: - "Black box" terminology: neural networks (AI) & brain complexity (neuroscience) - Plasticity concepts: model fine-tuning vs. synaptic plasticity - Emergent behavior in complex systems 3. GAP ANALYSIS: - Unexplored: How neuroplasticity principles could inform adaptive AI ethics - Methodological gap: Lack of interdisciplinary tools for analyzing AI-brain analogies - Theoretical blind spot: Consciousness and moral agency in AI systems 4. SYNTHESIS FRAMEWORK: "Neuro-Computational Ethics Model" integrating biological learning constraints with AI governance 5. RESEARCH QUESTIONS: - Can neuroplasticity-inspired algorithms reduce AI bias? - How do dopaminergic reward systems inform ethical AI reinforcement learning? - What neurological foundations underpin human trust in AI systems? 6. METHODOLOGY: Combined fMRI studies with AI model interpretability analysis Field Combination: Neuroscience & AI Ethics CONCEPTUAL BRIDGING: Both examine decision-making (neural vs algorithmic), bias (cognitive vs algorithmic), reward systems (dopamine vs RL). PATTERN RECOGNITION: "Black box" terminology shared; plasticity concepts parallel; emergent behavior in complex systems. GAP ANALYSIS: Unexplored - neuroplasticity principles for adaptive AI ethics; Methodological gap - tools for AI-brain analogies; Blind spot - consciousness in AI moral agency. SYNTHESIS FRAMEWORK: "Neuro-Computational Ethics Model" - biological learning constraints inform AI governance. RESEARCH QUESTIONS: Can neuroplasticity reduce AI bias? How do dopaminergic systems inform ethical RL? What neurological foundations underpin human trust in AI? METHODOLOGY: Combined fMRI + AI interpretability analysis IMPACT: Could revolutionize AI safety research and human-AI interaction design. 7. IMPACT: Could revolutionize AI safety research and human-AI interaction design.