The global landscape of AI ethics guidelines
The global landscape of AI ethics guidelines
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DOI：
10.1038/s42256-019-0088-2
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发表时间：
2019-09-01
影响因子：
23.8
通讯作者：
Vayena, Effy
中科院分区：
文献类型：
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作者：
Jobin, Anna;Ienca, Marcello;Vayena, Effy
在过去的五年里，私营公司、研究机构和公共部门组织已经发布了人工智能（AI）道德原则和指导方针。然而，尽管人们显然一致认为人工智能应该是“道德的”，但对于什么构成“道德人工智能”以及实现它需要哪些道德要求、技术标准和最佳实践存在争议。为了调查在这些问题上是否正在形成全球共识，我们绘制并分析了当前关于道德AI的原则和指导方针。我们的研究结果揭示了围绕五个道德原则（透明度，正义和公平，非恶意，责任和隐私）出现的全球趋同，在如何解释这些原则方面存在实质性分歧，为什么它们被认为是重要的，它们涉及什么问题，领域或行为者，以及它们应该如何实施。我们的研究结果强调了将指南制定工作与实质性道德分析和适当的实施战略相结合的重要性。随着人工智能技术的快速发展，人们普遍认识到，在社会中安全和公平地实施人工智能技术需要道德准则。但是，有可能就什么是“道德AI”达成一致吗？对来自国家和国际组织、公司和机构的84份人工智能道德报告进行了详细分析，探讨了这个问题，发现核心原则趋同，但实际实施存在重大分歧。
In the past five years, private companies, research institutions and public sector organizations have issued principles and guidelines for ethical artificial intelligence (AI). However, despite an apparent agreement that AI should be 'ethical', there is debate about both what constitutes 'ethical AI' and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analysed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted, why they are deemed important, what issue, domain or actors they pertain to, and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies. As AI technology develops rapidly, it is widely recognized that ethical guidelines are required for safe and fair implementation in society. But is it possible to agree on what is 'ethical AI'? A detailed analysis of 84 AI ethics reports around the world, from national and international organizations, companies and institutes, explores this question, finding a convergence around core principles but substantial divergence on practical implementation.