🤖 AI Summary
This study addresses the question of what user-relevant content should be included in local, post-hoc explanations for industrial AI systems and how such content should be organized. Through a hybrid inductive–deductive qualitative content analysis, the authors integrate user research data from six domains with explainable AI theory to develop the first user-oriented model comprising fourteen categories of local explanation content. The model encompasses rule-based and causal dimensions alongside two cognitive dimensions. It demonstrates high content validity and clear conceptual boundaries, as evidenced by expert review, an item-level content validity index (I-CVI ≥ 0.82), and strong intercoder reliability (Krippendorff’s α = Cohen’s κ = 0.920). This framework provides both theoretical grounding and practical guidance for the elicitation, standardization, and evaluation of explanation content in industrial AI applications.
📝 Abstract
Which categories of explanation content are relevant for users of industrial AI systems, and how can those categories be organized for local, post-hoc explanations? To address these questions, a hybrid inductive-deductive qualitative content analysis was applied to 325 meaning units drawn from six user studies in building technology, manufacturing, AI software development, and hospital cybersecurity. The inductive phase produced an initial twelve-code structure. A theory-informed coverage assessment and expert review then added two further codes, Rule base and What-if backward, that were not instantiated in the corpus but correspond to system architectures documented in the XAI literature. The resulting fourteen-code model is organized into four groups: rule-based, causal, epistemic (actual), and epistemic (similar), with twelve codes grounded in the corpus and two as theoretical extensions. An eleven-member expert panel supported the content adequacy of all codes (I-CVI $\geq$ 0.82; scale-level agreement of 0.93 for relevance, 0.92 for boundary clarity, and 0.94 for understandability). A stratified subsample of 82 units (25\% of the corpus), coded independently by two researchers using the finalized codebook, yielded Krippendorff's $α= 0.920$ and Cohen's $κ= 0.920$. The paper therefore establishes content adequacy and coding reproducibility for a content-level explanation model intended to support elicitation, specification, and later evaluation of explanation content in industrial AI systems. Behavioral validation of downstream effects remains future work.