Transparency and Explainability of AI Systems: Ethical Guidelines in Practice
Transparency and Explainability of AI Systems: Ethical Guidelines in Practice
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人工智能系统的透明度和可解释性：实践中的道德准则
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Sari Kujala
中科院分区：
文献类型：
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作者：
Nagadivya Balasubramaniam;Marjo Kauppinen;Kari Hiekkanen;Sari Kujala
. [背景和动机]最近的研究强调透明度和可解释性是人工智能系统的重要质量要求。然而，仍然有相对较少的案例研究，描述了在实践中定义这些质量要求的现状。我们研究的目的是探索组织为开发透明和可解释的人工智能系统定义了哪些道德准则。我们分析了代表不同行业和公共部门的16个组织的道德准则。[结果]在伦理准则中，几乎所有组织都强调了透明度的重要性，并将可解释性视为透明度的组成部分。在人工智能系统中建立信任是提高透明度和可解释性的关键原因之一，客户和用户被认为是解释的主要目标群体。这些组织还提到开发人员、合作伙伴和利益相关者是需要解释的重要群体。道德准则包含了人工智能系统的以下方面，应该加以解释：目的，人工智能的作用，输入，行为，使用的数据，输出和限制。该准则还指出，透明度和可解释性与其他几项质量要求有关，如可信性、可理解性、可追溯性、隐私性、可解释性和公平性。[贡献]对于研究人员来说，这篇论文提供了关于组织认为AI系统的透明度，特别是可解释性重要的见解。对于从业者来说，这项研究提出了一种结构化的方法来定义AI系统的可解释性要求。
. [Context and Motivation] Recent studies have highlighted transparency and explainability as important quality requirements of AI systems. However, there are still relatively few case studies that describe the current state of defining these quality requirements in practice. [Question] The goal of our study was to explore what ethical guidelines organizations have defined for the development of transparent and explainable AI systems. We analyzed the ethical guidelines in 16 organizations representing different industries and public sector. [Results] In the ethical guidelines, the importance of transparency was highlighted by almost all of the organizations, and explainability was considered as an integral part of transparency. Building trust in AI systems was one of the key reasons for developing transparency and explainability, and customers and users were raised as the main target groups of the explanations. The organizations also mentioned developers, partners, and stakeholders as important groups needing explanations. The ethical guidelines contained the following aspects of the AI system that should be explained: the purpose, role of AI, inputs, behavior, data utilized, outputs, and limitations. The guidelines also pointed out that transparency and explainability relate to several other quality requirements, such as trustworthiness, understandability, traceability, privacy, auditability, and fairness. [Contribution] For researchers, this paper provides insights into what organizations consider important in the transparency and, in particular, explainability of AI systems. For practitioners, this study suggests a structured way to define explainability requirements of AI systems.