Saliency Explanation

Saliency explanation aims to make the decisions of complex machine learning models, particularly deep neural networks, more transparent by identifying the input features most influential on the model's output. Current research focuses on developing and evaluating various saliency methods, often using gradient-based approaches or attention mechanisms within model architectures like U-Nets and Vision Transformers, and employing diverse evaluation metrics including human-centered assessments. This work is crucial for building trust in AI systems across various domains (image recognition, NLP, time series analysis, reinforcement learning) by improving model interpretability and facilitating the identification of biases or errors. The ultimate goal is to enhance the reliability and responsible deployment of AI technologies.