סיפור-מידע נגד פירוש-מכוני: פיענוח החלטות AI ללא-מומחים כדי לשמור על נתונים ופרטי-מודל

תקציר מקורי באנגליתarXiv:2609.15722v1 Announce Type: cross Abstract: AI-driven automated decision-making requires both predictive performance and interpretability. Recent advances in interpretable machine learning (IML) provide tools for explaining model predictions, but the technical complexity of these explanations may hinder accessibility to non-experts. To address this challenge, this study integrates data storytelling with IML to enhance the explainability of AI-generated decisions for a broader audience. Following the design science research (DSR) paradigm, this study proposes a formal definition of data storytelling in IML, introduces the DIST Pyramid to align data storytelling with IML, and presents the I-P-O Model to describe their interactions. It further develops an architecture to explain AI deci