Explainable AI (XAI) Quiz Questions

1. What is the primary goal of Explainable AI (XAI)?

view answer: B. To make AI systems more transparent and understandable
Explanation: Explainable AI (XAI) aims to make AI systems more transparent and understandable by humans. By providing clear explanations for the AI's decisions and actions, XAI helps to build trust in AI systems and allows for better human-AI collaboration.
2. Which of the following techniques is NOT commonly used in Explainable AI?

view answer: C. Random Forest
Explanation: Random Forest is a machine learning algorithm and not a technique specifically designed for Explainable AI. LIME, LRP, and SHAP are all techniques that help to explain the inner workings of AI models and make their predictions more interpretable to humans.
3. What does "model-agnostic" mean in the context of Explainable AI techniques?

view answer: B. The technique can be applied to any type of machine learning model
Explanation: In the context of Explainable AI, "model-agnostic" refers to techniques that can be applied to a wide variety of machine learning models. These techniques do not depend on the specific architecture or algorithm of the model, allowing them to be applied to different AI systems.
4. Why is it important to consider the audience when designing explainable AI solutions?

view answer: A. To ensure the explanations are relevant to the users' needs
Explanation: Considering the audience when designing explainable AI solutions is important because different users have different needs and levels of understanding. Providing explanations that are relevant and understandable to the target users helps to build trust, facilitate collaboration, and ensure effective use of the AI system.
5. What is the purpose of a feature visualization technique?

view answer: C. To provide a visual representation of the relationship between features and the output of a model
Explanation: A feature visualization technique is used to provide a visual representation of the relationship between features and the output of a model. It is often used in XAI to help understand how a model makes its predictions.
6. What is the purpose of a decision tree?

view answer: B. To provide an interpretable model that can be used for XAI
Explanation: A decision tree is an interpretable model that can be used for XAI. It is often used in XAI to provide a clear and understandable model for making predictions.
7. What is the purpose of a prototype instance?

view answer: B. To provide a representative example of a particular class or concept
Explanation: A prototype instance is a representative example of a particular class or concept. It is often used in XAI to provide a concrete example of the type of input that a model is designed to classify.
8. What is a model-agnostic explanation?

view answer: C. An explanation that is applicable to any model,regardless of its architecture or implementation
Explanation: A model-agnostic explanation is an explanation that is applicable to any model, regardless of its architecture or implementation. It is often used in XAI to provide a general understanding of how a model works.
9. What is a saliency map?

view answer: B. A map that shows the areas of an image that are most important for making a particular classification decision
Explanation: A saliency map is a map that shows the areas of an image that are most important for making a particular classification decision. It is often used in XAI to understand how a model makes its predictions.
10. What is the purpose of a confusion matrix?

view answer: C. To identify the number of true positive, false positive, true negative, and false negative predictions made by a model
Explanation: A confusion matrix is a table that summarizes the number of true positive, false positive, true negative, and false negative predictions made by a model. It is often used to evaluate the performance of a binary classification model.
11. What is the purpose of feature importance?

view answer: A. To identify which features are most important for making predictions
Explanation: Feature importance is a measure of the contribution of each feature to the model's prediction. It is often used in XAI to identify which features are most important for making predictions.
12. What is a gradient boosting machine (GBM)?

view answer: A. A type of ensemble model that combines the predictions of multiple weak models using a gradient descent algorithm
Explanation: A gradient boosting machine (GBM) is a type of ensemble model that combines the predictions of multiple weak models using a gradient descent algorithm. It is a white-box model that is often used in XAI.
13. What is a random forest?

view answer: A. A type of ensemble model that combines the predictions of multiple decision trees
Explanation: A random forest is a type of ensemble model that combines the predictions of multiple decision trees. It is a white-box model that is often used in XAI.
14. What is a global surrogate model?

view answer: A. A simple, interpretable model that is trained on the entire dataset to approximate the behavior of a more complex model
Explanation: A global surrogate model is a simple, interpretable model that is trained on the entire dataset to approximate the behavior of a more complex model. It is often used in XAI to provide explanations for black-box models.
15. What is the purpose of a partial dependence plot?

view answer: A. To visualize the relationship between a particular feature and the output of a model while holding all other features constant
Explanation: A partial dependence plot is a type of visualization that shows the relationship between a particular feature and the output of a model while holding all other features constant. It is often used in XAI to understand the behavior of a model and identify important features.
16. What is a decision boundary?

view answer: B. The line that separates the positive and negative classes in a binary classification problem
Explanation: A decision boundary is the line (or surface) that separates the positive and negative classes in a binary classification problem. It is determined by the model during the training process, and it is used to classify new data points. When a new data point is located on one side of the decision boundary, it is classified as belonging to the positive class, and when it is located on the other side, it is classified as belonging to the negative class. In other words, the decision boundary is used to make decisions about class membership based on the model's learned parameters.
17. What is a prototype explanation?

view answer: A. An explanation of why a model made a certain decision based on a prototype instance that represents a particular class
Explanation: A prototype explanation is an explanation of why a model made a certain decision based on a prototype instance that represents a particular class. It is a type of XAI technique that helps to make the decision-making process more transparent.
18. What is a surrogate model?

view answer: A. A simple, interpretable model that is used to approximate the behavior of a more complex model
Explanation: A surrogate model is a simple, interpretable model that is used to approximate the behavior of a more complex model. It is often used in XAI to provide explanations for black-box models.
19. What is an anchor explanation?

view answer: A. An explanation of why a model made a certain decision based on the anchor points of the input space
Explanation: An anchor explanation is an explanation of why a model made a certain decision based on the anchor points of the input space. It is a type of XAI technique that provides a simple and interpretable rule for making predictions.
20. What is LIME?

view answer: B. A technique for interpreting the predictions of a model by approximating it with a simple, interpretable model
Explanation: LIME (Local Interpretable Model-Agnostic Explanations) is a technique for interpreting the predictions of a model by approximating it with a simple, interpretable model.
21. What is SHAP?

view answer: B. A technique for interpreting the predictions of a model by approximating it with a simple, interpretable model
Explanation: SHAP (SHapley Additive exPlanations) is a technique for interpreting the predictions of a model by approximating it with a simple, interpretable model.
22. What is the purpose of a saliency map?

view answer: A. To highlight the important features in an input that contributed to a model's prediction
Explanation: A saliency map is a type of visualization that highlights the important features in an input that contributed to a model's prediction.
23. What is a counterfactual explanation?

view answer: A. An explanation of why a model made a certain decision based on the counterfactual scenario of changing a particular feature value
Explanation: A counterfactual explanation is an explanation of why a model made a certain decision based on the counterfactual scenario of changing a particular feature value. It is a type of "what-if" analysis that helps to make the decision-making process more transparent.
24. What is a white-box model?

view answer: A. A model that is transparent and easily interpretable
Explanation: A white-box model is a model that is transparent and easily interpretable. In other words, the internal workings of the model are easily understandable and transparent to humans.
25. What is a gray-box model?

view answer: C. A model that is partially transparent and only reveals certain aspects of its decision-making process
Explanation: A gray-box model is a model that is partially transparent and only reveals certain aspects of its decision-making process. It is somewhere between a black-box and a white-box model.
26. What is model interpretability?

view answer: B. The ability of a model to explain its predictions and decisions in a way that is understandable to humans
Explanation: Model interpretability refers to the ability of a model to explain its predictions and decisions in a way that is understandable to humans.
27. What is a decision tree?

view answer: A. A type of model that uses a tree-like structure to represent decisions and their consequences
Explanation: A decision tree is a type of model that uses a tree-like structure to represent decisions and their consequences. It is a white-box model that is often used in XAI.
28. What is the importance of XAI?

view answer: A. It allows humans to better understand and trust the decisions made by AI systems
Explanation: The importance of XAI is that it enables humans to better understand and trust the decisions made by AI systems, which is crucial for their adoption in many applications.
29. What is a black-box model?

view answer: B. A model that is completely opaque and difficult to interpret
Explanation: A black-box model is a model that is completely opaque and difficult to interpret. In other words, the internal workings of the model are not easily understandable or transparent to humans.
30. What is Explainable AI (XAI)?

view answer: A. A type of machine learning that is able to explain its predictions and decisions
Explanation: Explainable AI (XAI) refers to a set of techniques and methods that enable machine learning models to explain their predictions and decisions in a way that is understandable to humans.

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