语义原型：无需黑盒即可增强透明度
Semantic Prototypes: Enhancing Transparency Without Black Boxes
AI总结：
针对传统原型方法依赖亚符号数据与不透明潜空间、可解释性不足的问题，本文提出利用语义描述定义原型的新框架，在语义层聚类数据，经用户调研验证其在人类理解度和信息性上优于现有常用原型方法，可提升透明度与信任度。
摘要：
随着机器学习（ML）模型和数据集的复杂度不断提升，人们对增强可解释性与可理解性的方法的需求变得至关重要。原型通过封装数据中的核心特征，提供可支撑战术决策的洞见并提升透明度。传统原型方法通常依赖亚符号原始数据和不透明的潜在空间，降低了可解释性，还增加了误解风险。本文提出了一种新颖框架，利用语义描述来定义原型并提供清晰解释，有效解决了传统方法的缺陷。我们的方法借助基于概念的描述在语义层面聚类数据，确保原型不仅能直观表征底层属性，还易于解读。该方法简化了解释流程，有效弥合了复杂数据结构与人类认知过程之间的鸿沟，从而提升透明度并增进信任。一项用户调研证实，在促进人类理解和信息丰富度方面，我们的方法优于现有广泛使用的原型方法。
英文摘要：
As machine learning (ML) models and datasets increase in complexity, the demand for methods that enhance explainability and interpretability becomes paramount. Prototypes, by encapsulating essential characteristics within data, offer insights that enable tactical decision-making and enhance transparency. Traditional prototype methods often rely on sub-symbolic raw data and opaque latent spaces, reducing explainability and increasing the risk of misinterpretations. This paper presents a novel framework that utilizes semantic descriptions to define prototypes and provide clear explanations, effectively addressing the shortcomings of conventional methods. Our approach leverages concept-based descriptions to cluster data on the semantic level, ensuring that prototypes not only represent underlying properties intuitively but are also straightforward to interpret. Our method simplifies the interpretative process and effectively bridges the gap between complex data structures and human cognitive processes, thereby enhancing transparency and fostering trust. Our approach outperforms existing widely-used prototype methods in facilitating human understanding and informativeness, as validated through a user survey.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
