Explainable AI (XAI) QUIZ (MCQ QUESTIONS AND ANSWERS)

To ensure the explanations are relevant to the users' needs

To minimize the computational resources needed for explanations

To protect the privacy of the AI model's internal structure

To avoid the risk of overfitting the model

The technique is not biased toward any particular model

The technique can be applied to any type of machine learning model

The technique does not require knowledge of the model's internal structure

The technique does not improve the model's accuracy

Local Interpretable Model-agnostic Explanations (LIME)

Layer-wise Relevance Propagation (LRP)

Random Forest

Shapley Additive Explanations (SHAP)

To create AI systems that are more accurate

To make AI systems more transparent and understandable

To enhance the performance of AI systems on specific tasks

To reduce the computational complexity of AI systems

To generate synthetic data to improve the robustness of a model

To identify the most important features for making predictions

To provide a visual representation of the relationship between features and the output of a model

To measure the accuracy of a model

To generate synthetic data to improve the robustness of a model

To provide an interpretable model that can be used for XAI

To identify the most important features for making predictions

To measure the accuracy of a model

To visualize the decision boundaries of a model

To provide a representative example of a particular class or concept

To measure the accuracy of a model

To identify the most important features for making predictions

An explanation of why a model made a particular decision based on a prototype instance

An explanation of how a particular model works

An explanation that is applicable to any model,regardless of its architecture or implementation

An explanation of how to train a particular model

A visualization of the relationship between features and the output of a model

A map that shows the areas of an image that are most important for making a particular classification decision

A measure of the accuracy of a model

A visualization of the decision boundaries of a model

To visualize the relationship between features and the output of a model

To measure the accuracy of a model

To identify the number of true positive, false positive, true negative, and false negative predictions made by a model

To measure the performance of a model on a validation set

To identify which features are most important for making predictions

To generate new features to improve the performance of a model

To visualize the relationship between features and the output of a model

To measure the accuracy of a model

A type of ensemble model that combines the predictions of multiple weak models using a gradient descent algorithm

A linear model that uses a weighted sum of features to make predictions

A deep neural network with many layers

A type of model that uses generative adversarial networks (GANs)

A type of ensemble model that combines the predictions of multiple decision trees

A linear model that uses a weighted sum of features to make predictions

A deep neural network with many layers

A type of model that uses generative adversarial networks (GANs)

A simple, interpretable model that is trained on the entire dataset to approximate the behavior of a more complex model

A model that is trained on a subset of the data to improve its performance

A model that is trained on synthetic data to improve its robustness

A model that is trained using a generative adversarial network (GAN)

To visualize the relationship between a particular feature and the output of a model while holding all other features constant

To visualize the decision boundaries of a model

To measure the accuracy of a model

To generate new training data for a model

The region of an input space that corresponds to a particular class label

The line that separates the positive and negative classes in a binary classification problem

The threshold value used to make a binary classification decision

The gradient of a model's loss function

An explanation of why a model made a certain decision based on a prototype instance that represents a particular class

An explanation of why a model made a certain decision based on its internal state

An explanation of how a model was trained

An explanation of the limitations of a model

A simple, interpretable model that is used to approximate the behavior of a more complex model

A model that is trained on a subset of the data to improve its performance

A model that is trained on synthetic data to improve its robustness

A model that is trained using a generative adversarial network (GAN)

An explanation of why a model made a certain decision based on the anchor points of the input space

An explanation of why a model made a certain decision based on its internal state

An explanation of how a model was trained

An explanation of the limitations of a model

An explanation of why a model made a certain decision based on the counterfactual scenario of changing a particular feature value

An explanation of why a model made a certain decision based on its internal state

An explanation of how a model was trained

An explanation of the limitations of a model

To highlight the important features in an input that contributed to a model's prediction

To visualize the decision boundaries of a model

To measure the accuracy of a model

To generate new training data for a model

A technique for visualizing the decision boundaries of a model

A technique for interpreting the predictions of a model by approximating it with a simple, interpretable model

A technique for reducing the size of a model by removing unimportant features

A technique for improving the accuracy of a model by generating synthetic data

A technique for visualizing the decision boundaries of a model

A technique for interpreting the predictions of a model by approximating it with a simple, interpretable model

A technique for reducing the size of a model by removing unimportant features

A technique for improving the accuracy of a model by generating synthetic data

A type of model that uses a tree-like structure to represent decisions and their consequences

A type of model that uses deep learning to make decisions

A type of model that is completely opaque and difficult to interpret

A type of model that is based on quantum mechanics and cannot be understood by humans

The ability of a model to make accurate predictions

The ability of a model to explain its predictions and decisions in a way that is understandable to humans

The ability of a model to learn from new data

The ability of a model to generalize to new situations

A model that is transparent and easily interpretable

A model that is completely opaque and difficult to interpret

A model that is partially transparent and only reveals certain aspects of its decision-making process

A model that is based on quantum mechanics and cannot be understood by humans

A model that is transparent and easily interpretable

A model that is completely opaque and difficult to interpret

A model that is partially transparent and only reveals certain aspects of its decision-making process

A model that is based on quantum mechanics and cannot be understood by humans

A model that is transparent and easily interpretable

A model that is completely opaque and difficult to interpret

A model that is partially transparent and only reveals certain aspects of its decision-making process

A model that is based on quantum mechanics and cannot be understood by humans

It allows humans to better understand and trust the decisions made by AI systems

It helps to make AI systems more efficient and accurate

It is necessary for AI systems to function at all

It allows AI systems to operate autonomously without human intervention

A type of machine learning that is able to explain its predictions and decisions

A type of deep learning that is used to generate explanations

A type of reinforcement learning that is used to train explainable agents

A type of unsupervised learning that is used to discover hidden patterns