ScholarWorks@숙명여자대학교

초록

This paper proposes a novel framework for integrating ethical decision-making into AI systems using edge computing, reinforced by advanced middleware design and reinforcement learning algorithms. The framework addresses the growing need for real-time, ethical decision-making in distributed computing environments, particularly where AI systems must operate across diverse cultural and social contexts. Key components of the framework include a Bias Detection Algorithm(BDA) and a Self-Inspection Algorithm(SIA), which are embedded within the middleware layers to ensure the consistent application of ethical standards throughout the data processing and decision-making pipeline. The BDA operates at the data input stage to filter out biases, while the SIA evaluates and adjusts AI responses during scenario-based training. Reinforcement learning techniques are employed to enable the AI to adapt and improve its ethical decision-making capabilities over time, informed by continuous feedback from human-centered evaluations. The framework is designed to be scalable and adaptable, ensuring that AI systems can maintain high ethical standards even in complex and dynamic environments such as smart cities and autonomous vehicle networks. By integrating ethical considerations directly into the edge computing process, this approach enhances both the transparency and reliability of AI-driven decisions.

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