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Abstract
Architecting Responsible Development and Deployment of Generative AI" presents a comprehensive framework for ensuring the responsible development and deployment of generative artificial intelligence (AI) systems. The paper addresses various aspects crucial for the ethical and effective utilization of generative AI, ranging from governance frameworks and accountability measures to technical considerations such as explainability, fairness, and operational resilience. Through an in-depth exploration of topics such as monitoring and reporting systems, data suitability, performance evaluation metrics like ROUGE and METEOR, and transparency measures, the paper provides practical guidance for organizations and practitioners. Additionally, it delves into the importance of diversity metrics, benchmarking techniques, and user feedback mechanisms in promoting ethical AI practices. Furthermore, the paper outlines key architectural principles for ensuring modularity, scalability, fault tolerance, and efficient resource utilization in generative AI systems. By integrating legal compliance, consent management, and user interface design considerations, the framework aims to foster trust, mitigate risks, and promote the responsible advancement of generative AI technologies.
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