分析750+AI事故数据，提炼报告难题与改进方案
Lessons for Editors of AI Incidents from the AI Incident Database
- 梳理750+事故案例与两种分类体系，识别共性挑战
- 发现因果、伤害程度等信息常存在认知不确定性
- 提出增强报告鲁棒性的实践建议，适合监管与研究者
随着人工智能系统在全球范围内的广泛应用，其引发的事故事件（对个人与社会造成伤害的事件）也日益增多。为此，产业界、民间组织及各国政府正制定最佳实践与监管措施以监测和分析此类事故。AI Incident Database（AIID）是一个旨在归档AI事故并支持研究的项目，提供平台用于对事故进行分类，服务于不同运营与研究目标。本研究回顾了AIID中超过750起事故的数据集及两种独立的分类体系，识别出在事故索引与分析中常见的挑战。研究发现，某些事故模式存在结构性模糊性，导致事故数据库构建困难，并探讨了在事故报告中不可避免的认知不确定性。因此，本文提出了应对与因果、伤害范围、严重程度或技术细节相关不确定性的缓解策略。基于这些发现，讨论了未来如何建立更稳健的AI事故报告实践。
原文摘要 · Abstract (English)
As artificial intelligence (AI) systems become increasingly deployed across the world, they are also increasingly implicated in AI incidents - harm events to individuals and society. As a result, industry, civil society, and governments worldwide are developing best practices and regulations for monitoring and analyzing AI incidents. The AI Incident Database (AIID) is a project that catalogs AI incidents and supports further research by providing a platform to classify incidents for different operational and research-oriented goals. This study reviews the AIID's dataset of 750+ AI incidents and two independent taxonomies applied to these incidents to identify common challenges to indexing and analyzing AI incidents. We find that certain patterns of AI incidents present structural ambiguities that challenge incident databasing and explore how epistemic uncertainty in AI incident reporting is unavoidable. We therefore report mitigations to make incident processes more robust to uncertainty related to cause, extent of harm, severity, or technical details of implicated systems. With these findings, we discuss how to develop future AI incident reporting practices.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。