通过监管和设计使用问责制来提高人工智能可解释性的框架

Arsh Shah
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摘要

本文讨论了改进人工智能可解释性法规和框架的框架,借鉴了道德人工智能设计、自我监管、用于审计的区块链解决方案以及从Github派生的FAT(公平、问责制和透明度)取证包。该研究考察了GDPR、中国人工智能标准、美国法律和澳大利亚国内法(州和联邦层面)中人工智能的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Frameworks For Improving AI Explainability Using Accountability Through Regulation and Design
This paper discusses frameworks for improving AI explainability regulations and frameworks, drawing on ethical AI design, self-regulation, blockchain solutions for auditing, and FAT (fairness, accountability and transparency) Forensics packages forked from Github. The work takes a look at approaches to AI in the GDPR, Chinese AI Standards, United States law, and domestic Australian Law (at both the State and Federal Levels).
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