AI Conversational Alignment Assurance

2022年末/ 2023年初基于语言的人工智能模型的发展尤其显示出在文本生成和问答环境中的令人印象深刻的能力。然而，它们也显示出其局限性，并且在广泛和一般情况下有些不可靠。我们提议的项目旨在通过开发和测试方法和技术，以及控制层的软件组件来解决其中的一些缺陷，这些控制层管理和协调基于人工智能语言模型的特定组织系统的操作。这将使此类系统能够部署在面向目标的会话环境中，例如客户支持、服务和销售，以确保与组织的意图和品牌价值保持一致和一致。尽管商业领袖、公民社会领袖和公众成员对人工智能的潜在负面影响表示了普遍担忧，但缺乏制定和评估实际方法的举措。这个项目紧迫地解决了这个问题。我们的方法旨在解决与对话式人工智能系统的一致性、集成、合规性、风险、信任和可靠性相关的关键问题。我们的目标是开发可靠、安全、有效的对话式人工智能系统。这需要扩展传统保证的思想，因为它假设了测试、评估和审计中的确定性，因此需要将保证封装在解决方案框架中。

The late 2022/early 2023 developments in language-based AI models have especially revealed impressive competencies in text generation and question-answering contexts. However, they have also revealed their limitations and somewhat unreliable nature for wide and general contexts.Our proposed project aims to address some of these deficiencies by developing and testing methods and techniques, and software components for a control layer that manages and coordinates the operation of specific, organisational systems built on AI language models. This will enable such systems to be deployed in goal-oriented conversational contexts such as customer support, service, and sales in a way that assures alignment and compliance with an organisation's intent and brand values.Although business leaders, leaders of civic society, and members of the public have expressed general concerns about the potential negative impact of AI, initiatives to develop and evaluate practical approaches are lacking. This project addresses this with urgency.Our approach aims to address crucial issues related to the alignment, integration, compliance, risk, trust, and reliability of conversational AI systems. Our goal is to develop reliable, safe and effective conversational AI systems. This requires extending ideas of traditional assurance because of its assumptions about certainty in testing, evaluation and auditing, and consequent need to encapsulate assurance within solution frameworks.