xai Part II - Explainable ML Models: Local post hoc explanations Post hoc explanations approximate the behavior of a black-box by extracting relationships between feature values and the predictions. Several local explanation methods are model-agnostic, meaning they do not have access to the internal structure of the model.
xai Explainable ML Models: what are explanations and why do we need them? – Part I Interpretability is a key element of trust for AI models. An explanation is an interpretable description of a model behavior. For an explanation to be valid it needs to be faithful to the model and it needs to be understandable to the user.
artificial intelligence Redefining the Newsletter Experience A decline in trust in media and the need to access unbiased information, are two of the many reasons why a wave of local, reader-supported newspapers is emerging. Local news publishers build a closer tie with the readers by talking about the community, city development, and the local government they
