explainable ai

"機械学習モデルの解釈手法としてSHAP はかなりメジャーな選択肢です。特徴量ごとの寄与をサンプル単位でも全体傾向でも見られるためとりあえず SHAP を見ておく,という場面はかなり多いように思えます。"

"To address this, I developed SUT‑XR, an external evaluation framework for AI explanations. This is not a method for improving the AI itself, but a framework for managing the quality of its explanations."

"I designed the SUT-XR (External Rating Framework) to solve this problem by reversing the thinking: creating a layer to evaluate AI explanations from the outside, rather than improving the AI internally."

"We identify a Persuasion Paradox: fluent explanations systematically increase user confidence and reliance on AI without reliably improving, and in some cases undermining, task accuracy."

"Gartner predicts that by 2028, 50% of companies using Generative AI will invest in LLM observability."

"We believe that as AI evolves, people can become even freer."

"SHAP decomposes any ML model's predictions into feature contribution values."

"Our results demonstrate that the evolved GP models consistently achieve strong alignment between the predictions and human visual preferences."

"The entire AI world right now runs on "just trust us.""

"We present NOTAI.AI, an explainable framework for machine-generated text detection that extends Fast-DetectGPT by integrating curvature-based signals with neural and stylometric features in a supervised setting."

"DIG makes emergent collaboration observable and explainable for the first time, enabling real-time identification, explanation, and correction of collaboration-induced error patterns directly from agents' collaboration paths."

"In this work, we explore whether attack signals can be leveraged to improve the reliability of counterfactual explanations."

""Every token produced by the model can be traced back to its origins in the LLM’s training data.""

"We present InfoCIR, a visual analytics system that closes this gap by coupling retrieval, explainability, and prompt engineering in a single, interactive dashboard."

"Mythic Engine is a GPU-free, deterministic, and auditable inference engine."

"Results demonstrate that text explanations achieve higher comprehension efficiency, while voice explanations yield improved trust calibration, with analogy based delivery achieving the best overall trade off."

"Remember: An LLM is a deep artificial neural network, made up of neurons and weights that determine how strongly those neurons are connected."

"Results demonstrate that normalized surface distance dominates model predictions, contributing more than an order of magnitude greater importance than all other descriptors."

"I’m testing a modular reasoning stack (MRS Core) that forces a model to reason in discrete operators instead of one forward pass."

"This article promises a clear breakdown of the complex mathematical formulas"

"We conducted a formative study through 14 interviews with end users in education and medicine. We contribute a catalog of information and associated actions."

"The combination of visualization and textual narrative effectively enhanced user understanding (average score 4.31/5) and empo"

"Logic-oriented fuzzy neural networks are capable to cope with a fundamental challenge of fuzzy system modeling. They strike a sound balance between accuracy and interpretability because of the underlying features of the network components and their logic-oriented characteristics."

"This work demonstrates that combining interpretable fuzzy rules with feature importance explanations enhances both utility and trust, providing practical insights for XAI deployment in maternal healthcare."

"Experiments on a real-world image classification dataset demonstrate that EGT achieves up to 98.97% overall accuracy (matching baseline performance) with a 1.97x inference speedup through early exits, while improving attention consistency by up to 18.5% compared to baseline models."

"OmniNeuro is decoder-agnostic, acting as an essential interpretability layer for any state-of-the-art architecture."

"The paper focuses on secure and explainable fraud detection."

"The source is ArXiv, indicating a research paper."

"The research is sourced from ArXiv."