Description: The ‘Model Explanation’ refers to the ability of an artificial intelligence (AI) system to provide a clear and accessible understanding of the factors influencing its predictions. This concept is fundamental in the field of explainable AI, where the goal is to unravel the ‘black box’ that often characterizes machine learning models. ‘Model Explanation’ allows users, both experts and non-experts, to understand how and why a model has reached a particular conclusion. This includes identifying the most relevant features impacting the model’s decision, as well as interpreting the relationships between these features. Transparency in AI models not only fosters trust in automated decisions but is also crucial for complying with ethical and legal regulations. In a world where AI-based decisions can have significant consequences, ‘Model Explanation’ becomes an essential tool to ensure these decisions are fair, responsible, and understandable. Additionally, it facilitates the identification of biases in data and allows developers to continuously improve their models, ensuring they align with societal values and needs.
