Description: The ‘Linguistic Summary’ in the context of explainable AI refers to the ability of an artificial intelligence model to provide a clear and understandable description of its decisions and behaviors using natural language. This approach aims to make the internal processes of AI models more accessible to users, allowing them to understand how and why certain decisions are made. Through linguistic summaries, the factors influencing the model’s predictions can be broken down, facilitating the interpretation of complex results. This practice is essential in applications where transparency and trust are crucial, such as in the healthcare, financial, or legal sectors. By offering explanations in a format that humans can understand, it promotes more effective interaction between machines and users, helping to mitigate distrust towards AI and encouraging its adoption in various domains. In summary, the ‘Linguistic Summary’ acts as a bridge between the technical complexity of AI models and the need for clarity and understanding by users, which is fundamental for the development of responsible and ethical artificial intelligence systems.
