Towards Responsible AI in the Era of Generative AI: A Reference Architecture for Designing Foundation Model based Systems

The release of ChatGPT, Bard, and other large language model (LLM)-based chatbots has drawn huge attention on foundations models (FMs) worldwide. FMs are massive artificial intelligence (AI) models that are pre-trained on vast amounts of broad data and can be adapted to perform a wide variety of tasks [1]. With numerous projects already underway to explore their potential, it is widely predicted that FMs will serve as the fundamental building blocks for most future AI and artificial generative intelligence (AGI) systems. Many reusable solutions have been proposed to tackle various challenges in designing FM-based systems. However, there is a lack of systematic guidance on the architecture design of FM-based systems. The impact of integrating FMs into software architecture are not fully studied yet. Additionally, the FM's growing capabilities can eventually absorb the other components of AI systems, introducing the moving boundary and interface evolution challenges in architecture design. On the other hand, there are unique challenges on building responsible AI into the architecture of FM-based systems. First, accountability becomes more complex due to the involvement of multiple stakeholders.