數據與人工智慧治理：推動大型語言模型中的公平、倫理與正義

摘要

本文探討了系統性地治理、評估和量化機器學習模型全生命週期中偏見的方法，從初始開發與驗證到持續的生產監控及防護措施的實施。基於我們在大型語言模型（LLMs）偏見評估與測試套件（BEATS）上的基礎工作，作者們分享了LLMs中普遍存在的偏見與公平性相關缺口，並討論了數據與AI治理框架，以應對LLMs中的偏見、倫理、公平性及事實性問題。本文所述的數據與AI治理方法適用於實際的現實世界應用，能夠在LLMs投入生產部署前進行嚴格的基準測試，促進持續的實時評估，並主動治理LLM生成的回應。通過在AI開發的全生命週期中實施數據與AI治理，組織能顯著提升其生成式AI系統的安全性和責任感，有效降低歧視風險，防範潛在的聲譽或品牌損害。最終，我們希望通過本文，為推動創建和部署社會責任與倫理對齊的生成式人工智能應用做出貢獻。

English

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.

數據與人工智慧治理：推動大型語言模型中的公平、倫理與正義

Data and AI governance: Promoting equity, ethics, and fairness in large language models

摘要

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