Model Lifecycle (Explainable AI)

≡Description

The Model Lifecycle in the context of Explainable Artificial Intelligence (XAI) refers to the various stages that an AI model undergoes from its initial conception to its implementation and maintenance in a real-world environment. This cycle includes phases such as problem definition, data collection and preparation, model training, validation, deployment, and continuous monitoring. Each of these stages is crucial to ensure that the model is not only effective in its task but also understandable and transparent for end users. Explainable AI seeks to demystify the functioning of AI models, allowing users to understand how and why certain decisions are made. This is especially important in critical applications, such as medicine or justice, where automated decisions can have a significant impact on people's lives. Transparency in each phase of the model lifecycle helps build trust and facilitate the adoption of AI technologies, ensuring that the results are interpretable and justified. In summary, the Model Lifecycle is an essential framework that guides the development and implementation of AI models, promoting explainability and accountability in their use.