评估生成式人工智能系统在系统和社会中的社会影响

Evaluating the Social Impact of Generative AI Systems in Systems and Society

June 9, 2023

作者: Irene Solaiman, Zeerak Talat, William Agnew, Lama Ahmad, Dylan Baker, Su Lin Blodgett, Hal Daumé III, Jesse Dodge, Ellie Evans, Sara Hooker, Yacine Jernite, Alexandra Sasha Luccioni, Alberto Lusoli, Margaret Mitchell, Jessica Newman, Marie-Therese Png, Andrew Strait, Apostol Vassilev

cs.AI

摘要

跨模态的生成式人工智能系统，涵盖文本、图像、音频和视频等多种形式，具有广泛的社会影响，但目前尚无官方标准来评估这些影响以及应当评估哪些影响。我们致力于建立一种标准方法，用于评估任何模态的生成式人工智能系统，主要分为两大类：在没有预定应用的基础系统中可以评估的内容，以及在社会中可以评估的内容。我们描述了具体的社会影响类别以及如何在基础技术系统中进行评估，然后在人们和社会中进行评估。我们针对基础系统制定了七大社会影响类别的框架：偏见、刻板印象和表征性危害；文化价值和敏感内容；性能差异；隐私和数据保护；财务成本；环境成本；数据和内容管理劳动成本。建议的评估方法适用于所有模态，并分析了现有评估的局限性，为未来评估的必要投资提供了起点。我们提出了五大社会评估类别，每个类别都有自己的子类别：可信度和自主性；不平等、边缘化和暴力；权威集中；劳动和创造力；生态系统和环境。每个子类别都包括减少伤害的建议。我们同时正在为人工智能研究社区创建一个评估存储库，以便贡献现有的评估，按照给定的类别进行分类。此版本将在2023年ACM FAccT举办的CRAFT会议后进行更新。

English

Generative AI systems across modalities, ranging from text, image, audio, and video, have broad social impacts, but there exists no official standard for means of evaluating those impacts and which impacts should be evaluated. We move toward a standard approach in evaluating a generative AI system for any modality, in two overarching categories: what is able to be evaluated in a base system that has no predetermined application and what is able to be evaluated in society. We describe specific social impact categories and how to approach and conduct evaluations in the base technical system, then in people and society. Our framework for a base system defines seven categories of social impact: bias, stereotypes, and representational harms; cultural values and sensitive content; disparate performance; privacy and data protection; financial costs; environmental costs; and data and content moderation labor costs. Suggested methods for evaluation apply to all modalities and analyses of the limitations of existing evaluations serve as a starting point for necessary investment in future evaluations. We offer five overarching categories for what is able to be evaluated in society, each with their own subcategories: trustworthiness and autonomy; inequality, marginalization, and violence; concentration of authority; labor and creativity; and ecosystem and environment. Each subcategory includes recommendations for mitigating harm. We are concurrently crafting an evaluation repository for the AI research community to contribute existing evaluations along the given categories. This version will be updated following a CRAFT session at ACM FAccT 2023.