Scholarlink

The most popular methods in AI-machine learning paradigm are mainly black boxes. For user acceptance of these methods in various tasks, explanations are necessary for why an AI tool takes such a decision, as it is a matter of user trust in AI. Although dedicated explanation tools are being massively developed, the users and decision makers do not know which are the most appropriate for them. In this chapter, we will propose a taxonomy of the AI-explanation methods. This contribution is aimed at helping users in their choice of explanation methods for AI (XAI).

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Explainable AI in Image Classification Tasks: How to Choose the Best?

  • 章节

  • 发表日期：

    2025年

查看全文
  • Alexey Zhukov,
  • Jenny Benois-Pineau,
  • Romain Giot,
  • Romain Bourqui

摘要

The most popular methods in AI-machine learning paradigm are mainly black boxes. For user acceptance of these methods in various tasks, explanations are necessary for why an AI tool takes such a decision, as it is a matter of user trust in AI. Although dedicated explanation tools are being massively developed, the users and decision makers do not know which are the most appropriate for them. In this chapter, we will propose a taxonomy of the AI-explanation methods. This contribution is aimed at helping users in their choice of explanation methods for AI (XAI).