Data storytelling meets interpretable machine learning: Decoding AI decisions for non-experts without revealing sensitive data and model details
Paper recorded by Signals 4 on 2026-09-14 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-14 on arXiv · recorded by Signals 4 on 2026-09-15
Category: cs.CL · 自然语言处理 · first seen 2026-09-15
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
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Cite this page: Data storytelling meets interpretable machine learning: Decoding AI decisions for non-experts without revealing sensitive data and model details: the #48 most recent of 186 cs.CL papers we have recorded (as of 2026-09-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/data-storytelling-meets-interpretable-machine-learning-decoding-ai-decisions-for.html
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