Description: Model fidelity refers to the degree to which a computational or mathematical model accurately represents the real-world process it aims to simulate. This concept is fundamental in the field of explainable artificial intelligence, as high model fidelity implies that the decisions and predictions made by the model are more reliable and understandable for users. Fidelity can be assessed in terms of accuracy, consistency, and generalization capability of the model, meaning it should not only fit well to the training data but also be able to make accurate predictions on unseen data. The importance of model fidelity lies in its ability to facilitate the interpretation of results, allowing users to understand how and why certain decisions are made. This is especially critical in applications where automated decisions can have significant impacts, such as in various fields including medicine, criminal justice, and finance. In summary, model fidelity is an essential pillar for building artificial intelligence systems that are not only effective but also transparent and accountable.
