The Chai Platform's AI Safety Framework
Xiaoding Lu, Aleksey Korshuk, Zongyi Liu, William Beauchamp

TL;DR
This paper introduces Chai's integrated AI safety framework, combining safety principles like content safeguarding, robustness, and transparency to ensure responsible, ethical, and safe chatbot interactions in real-world applications.
Contribution
It presents a comprehensive AI safety framework tailored for chatbots, integrating established safety principles into Chai's platform and demonstrating its practical effectiveness.
Findings
Effective mitigation of risks in chatbot interactions
Enhanced user safety and data protection
Demonstrated real-world impact of safety measures
Abstract
Chai empowers users to create and interact with customized chatbots, offering unique and engaging experiences. Despite the exciting prospects, the work recognizes the inherent challenges of a commitment to modern safety standards. Therefore, this paper presents the integrated AI safety principles into Chai to prioritize user safety, data protection, and ethical technology use. The paper specifically explores the multidimensional domain of AI safety research, demonstrating its application in Chai's conversational chatbot platform. It presents Chai's AI safety principles, informed by well-established AI research centres and adapted for chat AI. This work proposes the following safety framework: Content Safeguarding; Stability and Robustness; and Operational Transparency and Traceability. The subsequent implementation of these principles is outlined, followed by an experimental analysis of…
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Taxonomy
TopicsEthics and Social Impacts of AI · AI in Service Interactions
