Explainability in AI-Generated Visual Art
Explainability, or interpretability, in AI refers to understanding how and why an AI model produces specific outputs. In the context of visual art, explainability helps artists and developers gain insights into the decision-making processes of AI models. Techniques like activation mapping, feature visualization, and Layer-wise Relevance Propagation (LRP) are used to identify which parts of the input data influence the model's output the most. This not only aids in refining models for better performance but also addresses ethical concerns related to AI transparency and bias.