扩展AI公平性评估标准，覆盖图像、文本和生成式AI
Enhancements for Developing a Comprehensive AI Fairness Assessment Standard
- 拓展TEC标准，新增对图像、非结构化文本和生成式AI的评估维度
- 提升标准适用性，支持6G时代复杂AI系统公平性审查
- 适合关注AI伦理与合规的开发者、政策制定者参考
随着人工智能在通信、金融、医疗和公共服务等关键领域的作用日益增强，确保决策公平性对于避免对弱势群体造成不公正影响至关重要。这一需求在迈向6G时代尤为紧迫，届时AI将驱动自主网络管理与超个性化服务等复杂功能。现有的TEC AI系统公平性评估与评级标准主要针对表格数据和监督学习模型，但随着AI应用多样化，该标准亟需升级以增强影响力并扩大适用范围。本文提出对TEC标准进行扩展，纳入对图像、非结构化文本及生成式AI（包括大语言模型）的公平性评估，构建更全面的评估框架，以适应不断演进的人工智能技术。新框架有助于推动各行业负责任、可信赖的AI部署。
原文摘要 · Abstract (English)
As AI systems increasingly influence critical sectors like telecommunications, finance, healthcare, and public services, ensuring fairness in decision-making is essential to prevent biased or unjust outcomes that disproportionately affect vulnerable entities or result in adverse impacts. This need is particularly pressing as the industry approaches the 6G era, where AI will drive complex functions like autonomous network management and hyper-personalized services. The TEC Standard for Fairness Assessment and Rating of AI Systems provides guidelines for evaluating fairness in AI, focusing primarily on tabular data and supervised learning models. However, as AI applications diversify, this standard requires enhancement to strengthen its impact and broaden its applicability. This paper proposes an expansion of the TEC Standard to include fairness assessments for images, unstructured text, and generative AI, including large language models, ensuring a more comprehensive approach that keeps pace with evolving AI technologies. By incorporating these dimensions, the enhanced framework will promote responsible and trustworthy AI deployment across various sectors.
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