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<p>As artificial intelligence (AI) systems become more integrated into decision-making processes, the need for explainability has emerged to foster trust, understanding, and effective human-AI collaboration. With the variety of explainable AI (XAI) methods available, selecting the right one for a specific user group and a given use case remains challenging, especially given the limited empirical validation of existing theoretical guidance. This systematic literature review addresses this gap by synthesizing human-grounded evaluations of XAI methods to identify the influence of specific explanation properties on user outcomes across diverse settings. Moving beyond high-level taxonomies, we classify XAI methods along multiple dimensions, such as scope and output type. Based on this classification, we analyze how the properties of XAI methods affect different user groups, tasks, and domains. Our findings underscore the necessity of context-aware method selection, as the effectiveness of XAI methods varies significantly across use cases. Moreover, our analysis reveals imbalances in the existing empirical landscape, where certain methods and user groups are overrepresented while others are largely overlooked. By validating theoretical proposals with empirical evidence, this review provides actionable guidance for selecting XAI methods that are aligned with user needs and use case demands, paving the way for more targeted and effective human-AI interaction.</p>

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A review on empirical studies in explainable artificial intelligence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月3日

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  • Tobias Jahn,
  • Philipp Hühn,
  • Manfred Reichert

摘要

As artificial intelligence (AI) systems become more integrated into decision-making processes, the need for explainability has emerged to foster trust, understanding, and effective human-AI collaboration. With the variety of explainable AI (XAI) methods available, selecting the right one for a specific user group and a given use case remains challenging, especially given the limited empirical validation of existing theoretical guidance. This systematic literature review addresses this gap by synthesizing human-grounded evaluations of XAI methods to identify the influence of specific explanation properties on user outcomes across diverse settings. Moving beyond high-level taxonomies, we classify XAI methods along multiple dimensions, such as scope and output type. Based on this classification, we analyze how the properties of XAI methods affect different user groups, tasks, and domains. Our findings underscore the necessity of context-aware method selection, as the effectiveness of XAI methods varies significantly across use cases. Moreover, our analysis reveals imbalances in the existing empirical landscape, where certain methods and user groups are overrepresented while others are largely overlooked. By validating theoretical proposals with empirical evidence, this review provides actionable guidance for selecting XAI methods that are aligned with user needs and use case demands, paving the way for more targeted and effective human-AI interaction.