数据与人工智能治理：促进大型语言模型中的公平、伦理与公正
Data and AI governance: Promoting equity, ethics, and fairness in large language models
AI总结：
本文提出数据与AI治理框架，基于BEATS评估套件，覆盖LLM全生命周期，以系统量化偏见、确保公平伦理，提升生成式AI安全性与责任感。
摘要：
本文涵盖了在机器学习模型完整生命周期中系统治理、评估和量化偏见的各种方法，涵盖从初始开发和验证到持续的生产监控及护栏实施。基于我们先前在大型语言模型偏见评估与测试套件（BEATS）方面的工作，作者分享了大型语言模型（LLMs）中普遍存在的偏见与公平性相关差距，并讨论了旨在解决LLMs中偏见、伦理、公平性和事实性的数据与人工智能治理框架。本文讨论的数据与人工智能治理方法适用于实际现实世界应用，能够在生产部署前对LLMs进行严格基准测试，促进持续实时评估，并主动治理LLM生成的响应。通过在AI开发生命周期中实施数据与人工智能治理，组织可以显著增强其生成式AI系统的安全性和责任感，有效降低歧视风险，并防范潜在的名誉或品牌相关损害。最终，通过本文，我们旨在推动创建和部署具有社会责任感和伦理对齐的生成式人工智能驱动应用的进步。
英文摘要：
In this paper, we cover approaches to systematically govern, assess and quantify bias across the complete life cycle of machine learning models, from initial development and validation to ongoing production monitoring and guardrail implementation. Building upon our foundational work on the Bias Evaluation and Assessment Test Suite (BEATS) for Large Language Models, the authors share prevalent bias and fairness related gaps in Large Language Models (LLMs) and discuss data and AI governance framework to address Bias, Ethics, Fairness, and Factuality within LLMs. The data and AI governance approach discussed in this paper is suitable for practical, real-world applications, enabling rigorous benchmarking of LLMs prior to production deployment, facilitating continuous real-time evaluation, and proactively governing LLM generated responses. By implementing the data and AI governance across the life cycle of AI development, organizations can significantly enhance the safety and responsibility of their GenAI systems, effectively mitigating risks of discrimination and protecting against potential reputational or brand-related harm. Ultimately, through this article, we aim to contribute to advancement of the creation and deployment of socially responsible and ethically aligned generative artificial intelligence powered applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
