Line by Line Explanation

Line-by-line explanation in AI focuses on providing understandable justifications for model predictions, aiming to improve trust, transparency, and user understanding. Current research investigates various explanation methods, including those based on feature importance, example-based approaches, and symbolic reasoning, often applied to models like neural networks, transformers, and large language models. This work is crucial for building reliable and trustworthy AI systems across diverse applications, from recruitment and healthcare to education and business, by addressing issues like misinformation and model bias through improved interpretability.