Explainable AI: Interpreting and Understanding Machine Learning Models

EasyChair Preprint 12571

Abstract

Explainable AI (XAI) has emerged as a crucial field of research in machine learning, aiming to address the black-box nature of complex models and provide human-interpretable explanations for their decisions. As machine learning models become increasingly sophisticated and deployed in critical domains, such as healthcare, finance, and autonomous systems, there is a growing demand for transparency and accountability in their decision-making processes. This abstract provides an overview of the concept of explainable AI, highlighting its significance, key challenges, and various techniques used to interpret and understand machine learning models.

The abstract begins by emphasizing the importance of interpretability in machine learning models. While highly accurate models like deep neural networks have achieved remarkable performance across numerous domains, their decision-making processes often lack transparency, hindering their adoption in real-world applications. Explainability is crucial to building trust, ensuring fairness, and avoiding potential biases and discrimination in automated decision systems.

Next, the abstract discusses the challenges associated with building interpretable models. It explores the trade-off between model complexity and interpretability and the inherent tension between accuracy and explainability. Furthermore, it highlights the need to strike a balance between model transparency and preserving privacy and proprietary information.

Keyphrases: deep, learning, machine

@booklet{EasyChair:12571,
  author    = {Favour Olaoye and Kaledio Potter and Lucas Doris},
  title     = {Explainable AI: Interpreting and Understanding Machine Learning Models},
  howpublished = {EasyChair Preprint 12571},
  year      = {EasyChair, 2024}}