AI中的问责制：从原则到特定行业的认证
Accountability in AI: From Principles to Industry-specific Accreditation
摘要：
最近与人工智能相关的丑闻使人们关注人工智能的问责制，公众的兴趣和关注越来越多。本文借鉴了公共政策和治理的相关文献，做出了两方面的贡献。首先，我们提出了一个人工智能问责生态系统，作为系统的一个有用的镜头，不同的利益相关者需要并促进特定的问责机制。我们认为，目前的生态系统是不平衡的，需要通过人工智能的可解释性和足够的文件和流程形式化来提高透明度，以支持内部审计，最终导致外部认证流程。其次，我们使用赌博行业的案例研究来说明整个生态系统的一个子集，需要特定于行业的问责制原则和流程。我们严格定义和评估的关键问责制原则在赌博行业的实施，即解决算法偏见和模型的可解释性，然后根据我们的研究结果总结和讨论未来的工作方向。关键词：问责制，可解释的人工智能，算法偏见，监管。
英文摘要：
Recent AI-related scandals have shed a spotlight on accountability in AI, with increasing public interest and concern. This paper draws on literature from public policy and governance to make two contributions. First, we propose an AI accountability ecosystem as a useful lens on the system, with different stakeholders requiring and contributing to specific accountability mechanisms. We argue that the present ecosystem is unbalanced, with a need for improved transparency via AI explainability and adequate documentation and process formalisation to support internal audit, leading up eventually to external accreditation processes. Second, we use a case study in the gambling sector to illustrate in a subset of the overall ecosystem the need for industry-specific accountability principles and processes. We define and evaluate critically the implementation of key accountability principles in the gambling industry, namely addressing algorithmic bias and model explainability, before concluding and discussing directions for future work based on our findings. Keywords: Accountability, Explainable AI, Algorithmic Bias, Regulation.
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。
