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Modelling GDPR-Compliant Explanations for Trustworthy AI

机译:为可信赖的AI建模符合GDPR的解释

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Through the General Data Protection Regulation (GDPR), the European Union has set out its vision for Automated Decision-Making (ADM) and AI, which must be reliable and human-centred. In particular we are interested on the Right to Explanation, that requires industry to produce explanations of ADM. The High-Level Expert Group on Artificial Intelligence (AI-HLEG), set up to support the implementation of this vision, has produced guidelines discussing the types of explanations that are appropriate for user-centred (interactive) Explanatory Tools. In this paper we propose our version of Explanatory Narratives (EN), based on user-centred concepts drawn from ISO 9241, as a model for user-centred explanations aligned with the GDPR and the AI-HLEG guidelines. Through the use of ENs we convert the problem of generating explanations for ADM into the identification of an appropriate path over an Explanatory Space, allowing explainees to interactively explore it and produce the explanation best suited to their needs. To this end we list suitable exploration heuristics, we study the properties and structure of explanations, and discuss the proposed model identifying its weaknesses and strengths.
机译:欧盟通过《通用数据保护条例》(GDPR),确立了对自动化决策(ADM)和AI的愿景,这些愿景必须可靠且以人为本。我们尤其对解释权感兴趣,该权利要求行业提供ADM的解释。为支持实现这一愿景而成立的人工智能高级专家组(AI-HLEG)已制定了准则,讨论了适用于以用户为中心(交互式)的解释工具的解释类型。在本文中,我们基于ISO 9241中以用户为中心的概念,提出了我们的解释性叙述(EN)版本,作为与GDPR和AI-HLEG指南一致的以用户为中心的解释的模型。通过使用EN,我们将为ADM生成解释的问题转换为在解释空间上确定适当路径的问题,从而使被解释者可以交互式地探索它并产生最适合其需求的解释。为此,我们列出了合适的探索启发法,研究了解释的性质和结构,并讨论了确定其缺点和优势的提议模型。

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