<![CDATA[Purpose: This study aims to examine the effectiveness of AI-driven influencer marketing, develop an Xception-based Convolutional Neural Network (CNN) for deepfake detection, and analyze ethical and legal responsibility for artificial intelligence use from a rule-of-law perspective. Methods: A mixed-methods approach was employed by integrating Computer Science, Management, and Law perspectives. Quantitative analysis used Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among AI Personalization, AI Interaction, User Experience, and Trust. The Xception-based CNN was evaluated using standard classification metrics. Qualitative analysis involved expert interviews and examination of legal principles concerning AI governance, transparency, accountability, and consumer protection. Result: The Xception-based CNN achieved 97.12% accuracy and an AUC of 0.9920 in distinguishing authentic from AI-generated content. PLS-SEM results indicate that AI Interaction significantly influences User Experience and Trust, while User Experience has the strongest effect on Trust (β = 0.520; p < 0.001). The model explains 53.3% of the variance in Trust, and User Experience significantly mediates the relationship between AI Interaction and Trust. The legal analysis highlights the need for stronger AI governance addressing transparency, disclosure of AI-generated content, consent, accountability, and consumer protection. Novelty: The novelty of this study lies in integrating deepfake detection, AI-driven influencer marketing, consumer trust analysis, and rule-of-law perspectives into a unified framework for responsible digital marketing. This integrated approach provides a multidisciplinary perspective for balancing technological innovation with ethical responsibility, legal accountability, and consumer protection.]]>
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