Toward Ethical and Faith-Aware Emotional Intelligence in Machines: Conceptualizing the FIWF Algorithm

Recent advances in Artificial Intelligence (AI) have expanded affective computing and emotion recognition, yet most existing systems remain ethically opaque, culturally limited, and spiritually neutral. This paper conceptualizes the Faith-Integrated Weighted Fusion (FIWF) algorithm as a pathway toward ethical and faith-aware emotional intelligence in machines. FIWF serves as the core of FaithAI, an edge-deployable, explainable, and faith-aligned emotion-aware recommendation system that unifies multimodal emotion recognition, explainable AI (XAI), and faith-based counselling. Technically, FIWF applies faith-sensitive rules to weight multimodal inputs-text, audio, image, and video-for emotion inference, enhancing interpretability and fairness. Implemented using TensorFlow Lite and Local Interpretable Model-Agnostic Explanations (LIME), FaithAI operates entirely on-device via Edge AI, ensuring privacy and real-time responsiveness. Empirical evaluation demonstrated 91.7% emotion classification accuracy with a 124 ms latency and a 13.5% reduction in demographic bias compared to standard fusion methods. Grounded in ethical AI principles and intercultural theology, FIWF operationalizes transparency, fairness, and spiritual inclusivity within emotion-aware systems. By embedding cognitive, affective, and faith dimensions into its reasoning process, FIWF advances a model of value-aligned machine empathy, supporting contextually meaningful, faith-sensitive emotional interventions. This study thus contributes both a conceptual and practical foundation for human-centered, ethically responsible, and spiritually intelligent AI.