Quiz: The Explainable Artificial Intelligence Challenge

An advantage of Explainable AI is that it increases transparency by providing insight into the decision-making process of AI models.

This is significant because it helps to reduce any ethical issues, such as why it misidentified an item or failed to fire on a target.

It helps characterize model accuracy, fairness, transparency, and outcomes in AI-powered decision making.

Explainable AI faces several challenges including the trade-off between accuracy and explainability, lack of standard metrics, and interpretability complexity.

The Shapley value is the average marginal contribution of a feature value across all possible coalitions

Explainable AI refers to systems that can clearly articulate their internal processes and decision-making.

The explainable AI method LIME (Local Interpretable Model-agnostic Explanations) helps to illuminate a machine learning model and to make its predictions individually comprehensible.

LIME generates an explanation for a prediction from the components of an interpretable model (for instance, the coefficients in a linear regression) which resembles the black-box model at the vicinity of the point of interest and which is trained over a new data representation to ensure interpretability.

Each counterfactual provides information about how the outputs of a machine learning model would have been different, under counterfactual changes to the model's input variables

The main disadvantage of Explainable AI is that it can be computationally intensive, especially for complex models.