Explainable AI: Bridging the Gap between Machine Learning Models and Human Understanding

Journal of Informatics Education and Research, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

Explainable AI (XAI) is one of the key game-changing features in machine learning models, which contribute to making them more transparent, regulated and usable different applications. In (the) investigation this paper, we consider four rows explanation methods—LIME, SHAP, Anchor, Decision Tree-based Explanation—in disentangling decision-making process black box models within fields. our experiments, use datasets that cover domains, for example, health, finance image classification, compare accuracy, fidelity, coverage, precision human satisfaction each method. Our work shows rule trees approach called (Decision explanation) mostly superior comparison other non-model-specific methods performing higher coverage regardless classifier. addition this, respondents who answered qualitative evaluation indicated they were very content with decision tree-based explanations these types are easy understandable. Furthermore, most famous sorts clarifications instinctive significant. The over discoveries stretch on utilize interpretable strategies facilitating hole between understanding thus advancing straightforwardness responsibility AI-driven decision-making.

Язык: Английский

Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go?

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 266 - 287

Опубликована: Янв. 1, 2025

Язык: Английский