Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns
Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns
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DOI：
10.1177/2053951719860542
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发表时间：
2019-06-01
影响因子：
8.5
通讯作者：
Tamo-Larrieux, Aurelia
中科院分区：
文献类型：
--
作者：
Felzmann, Heike;Villaronga, Eduard Fosch;Tamo-Larrieux, Aurelia
透明度现在是《通用数据保护条例》下数据处理的基本原则。我们探讨这一要求对人工智能和自动化决策系统意味着什么。我们通过整合法律、社会和道德方面来解决人工智能透明度的主题。我们首先调查《通用数据保护条例》中透明度要求的比例规律及其道德基础，表明其重点是提供信息和解释。然后，我们通过重点关注实施透明度中背景和执行因素的重要性来讨论与此要求相关的陷阱。我们表明，由于广泛的背景因素（包括执行方面）的影响，人机交互和人机交互文献没有就人工智能技术用户的透明度的好处提供明确的结果。最后，我们将基于信息和解释的透明度方法与关键情境方法相结合，提出《通用数据保护条例》所要求的透明度本身可能不足以实现与透明度相关的积极目标。相反，我们建议以关系的方式理解透明度，其中信息提供被概念化为技术提供商和用户之间的通信，并且基于上下文因素的可信度评估调节透明度通信的价值。这种透明度的关系概念指出了人工智能系统透明度研究的未来研究方向，应该在政策制定中予以考虑。
Transparency is now a fundamental principle for data processing under the General Data Protection Regulation. We explore what this requirement entails for artificial intelligence and automated decision-making systems. We address the topic of transparency in artificial intelligence by integrating legal, social, and ethical aspects. We first investigate the ratio legis of the transparency requirement in the General Data Protection Regulation and its ethical underpinnings, showing its focus on the provision of information and explanation. We then discuss the pitfalls with respect to this requirement by focusing on the significance of contextual and performative factors in the implementation of transparency. We show that human-computer interaction and human-robot interaction literature do not provide clear results with respect to the benefits of transparency for users of artificial intelligence technologies due to the impact of a wide range of contextual factors, including performative aspects. We conclude by integrating the information- and explanation-based approach to transparency with the critical contextual approach, proposing that transparency as required by the General Data Protection Regulation in itself may be insufficient to achieve the positive goals associated with transparency. Instead, we propose to understand transparency relationally, where information provision is conceptualized as communication between technology providers and users, and where assessments of trustworthiness based on contextual factors mediate the value of transparency communications. This relational concept of transparency points to future research directions for the study of transparency in artificial intelligence systems and should be taken into account in policymaking.