where does logic pro x download additional content

Through the lens of literature, we review existing approaches regarding the topic, we discuss trends surrounding its sphere and we present major research trajectories. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to explainable AI. It is recognized as the sine qua non for AI to continue making steady progress without disruption. A research field that holds substantial promise for improving trust and transparency of AI-based systems.

This issue has triggered a new debate on Explainable Artificial Intelligence. Indeed, the black box nature of these systems allows powerful predictions, but it cannot be directly explained. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. We provide several examples of linear and non-linear models with various degrees of feature dependence, where our method gives more accurate approximations to the true Shapley values.Īt the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of Artificial Intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. In this paper, we extend the Kernel SHAP method to handle dependent features. This is the case even if a simple linear model is used for predictions. Since Shapley values currently suffer from inclusion of unrealistic data instances when features are correlated, the explanations may be very misleading. Like several other existing methods, this approach assumes that the features are independent. Kernel SHAP is a computationally efficient approximation to Shapley values in higher dimensions.

The Shapley value framework has a series of desirable theoretical properties, and can in principle handle any predictive model. Shapley values is a game theoretic concept that can be used for this purpose. We want to explain individual predictions from such models by learning simple, interpretable explanations.

Explaining complex or seemingly simple machine learning models is an important practical problem.

Where does logic pro x download additional content