AI Conversational Alignment Assurance

2022年底/2023年初基于语言的人工智能模型的发展尤其显示了文本生成和问答环境方面令人印象深刻的能力。然而，它们也暴露了它们的局限性，并且在广泛和一般的背景下有些不可靠。我们提出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，该控制层管理和协调基于AI语言模型构建的特定组织系统的操作。这将使这些系统能够部署在以目标为导向的对话环境中，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，公民社会领导人和公众对人工智能的潜在负面影响表示普遍担忧，但缺乏开发和评估实用方法的举措。我们的方法旨在解决与对话式AI系统的对齐、集成、合规、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，安全和有效的对话AI系统。这需要扩展传统保证的思想，因为它假设测试、评估和审计的确定性，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.