Explainable AI, often abbreviated as XAI, refers to a subset of artificial intelligence designed to make the decision-making processes of AI systems transparent and understandable to humans. Unlike traditional “black-box” models that produce outputs without clear reasoning, XAI employs techniques to reveal how and why a model arrives at specific conclusions. This transparency is essential in fields like healthcare, finance, and autonomous systems, where trust, accountability, and ethical considerations are paramount.
For instance, in medical applications, an XAI system might not only diagnose a condition but also highlight the key data points—such as specific symptoms or test results—that influenced its decision. Common methods include model-agnostic approaches like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), which provide interpretable insights into complex algorithms. By fostering better human-AI interaction, explainable AI helps mitigate biases, enhance debugging, and ensure regulatory compliance, ultimately building greater confidence in AI technologies.
Table of contents
- Part 1: Create an amazing explainable AI quiz using AI instantly in OnlineExamMaker
- Part 2: 20 explainable AI quiz questions & answers
- Part 3: Save time and energy: generate quiz questions with AI technology
Part 1: Create an amazing explainable AI quiz using AI instantly in OnlineExamMaker
The quickest way to assess the explainable AI knowledge of candidates is using an AI assessment platform like OnlineExamMaker. With OnlineExamMaker AI Question Generator, you are able to input content—like text, documents, or topics—and then automatically generate questions in various formats (multiple-choice, true/false, short answer). Its AI Exam Grader can automatically grade the exam and generate insightful reports after your candidate submit the assessment.
Overview of its key assessment-related features:
● Create up to 10 question types, including multiple-choice, true/false, fill-in-the-blank, matching, short answer, and essay questions.
● Automatically generates detailed reports—individual scores, question report, and group performance.
● Instantly scores objective questions and subjective answers use rubric-based scoring for consistency.
● API and SSO help trainers integrate OnlineExamMaker with Google Classroom, Microsoft Teams, CRM and more.
Automatically generate questions using AI
Part 2: 20 explainable AI quiz questions & answers
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1. Question: What is the primary goal of Explainable AI (XAI)?
A. To maximize computational speed
B. To make AI decisions transparent and understandable
C. To reduce the size of AI models
D. To eliminate human involvement in AI processes
Answer: B
Explanation: The primary goal of XAI is to provide insights into how AI models arrive at decisions, fostering trust and accountability by making complex processes interpretable to humans.
2. Question: Which technique uses local surrogate models to explain the predictions of a black-box AI model?
A. Decision Trees
B. LIME (Local Interpretable Model-agnostic Explanations)
C. Neural Networks
D. Random Forests
Answer: B
Explanation: LIME approximates the behavior of a complex model locally around a specific prediction by creating a simpler, interpretable model, thus explaining individual decisions.
3. Question: In XAI, what does the term “post-hoc explanation” refer to?
A. Explaining AI decisions before they are made
B. Building inherently interpretable models from the start
C. Providing explanations after the AI model has made a prediction
D. Training models to avoid predictions altogether
Answer: C
Explanation: Post-hoc explanations are generated after a model’s prediction, allowing for analysis of decisions made by black-box models without altering their structure.
4. Question: Which of the following is a key benefit of XAI in healthcare?
A. Faster data processing
B. Improved patient trust in AI-driven diagnoses
C. Reduced need for medical experts
D. Lowering the cost of AI hardware
Answer: B
Explanation: XAI helps in healthcare by making AI recommendations understandable, which builds trust and allows doctors to verify decisions, potentially reducing errors.
5. Question: What is SHAP (SHapley Additive exPlanations) based on?
A. Game theory
B. Quantum computing
C. Statistical sampling
D. Genetic algorithms
Answer: A
Explanation: SHAP is derived from cooperative game theory, assigning each feature an importance value based on its contribution to the model’s output, providing fair and consistent explanations.
6. Question: Why might XAI be less necessary for simple linear regression models?
A. They are already inherently interpretable
B. They require more data than complex models
C. They are only used in research
D. They always produce inaccurate results
Answer: A
Explanation: Simple linear regression models have transparent coefficients that directly show the relationship between inputs and outputs, making explanations straightforward without additional tools.
7. Question: In XAI, what role do feature importance scores play?
A. They predict future trends
B. They highlight which input features most influence the model’s decision
C. They measure the model’s speed
D. They reduce the dataset size
Answer: B
Explanation: Feature importance scores help users understand which variables drive the AI’s predictions, enabling better interpretation and debugging of the model.
8. Question: Which XAI method visualizes decision boundaries in classification problems?
A. Partial Dependence Plots
B. Gradient Descent
C. Backpropagation
D. Clustering algorithms
Answer: A
Explanation: Partial Dependence Plots show the relationship between a feature and the predicted outcome while averaging out other features, helping visualize how decisions are made.
9. Question: What is a common challenge in implementing XAI for deep learning models?
A. Overly simple explanations
B. Balancing accuracy with interpretability
C. Excessive speed in processing
D. Limited data availability
Answer: B
Explanation: Deep learning models often sacrifice interpretability for high accuracy, making it challenging to explain decisions without compromising performance.
10. Question: How does XAI differ from traditional AI transparency?
A. Traditional AI is always explainable
B. XAI focuses on user-friendly explanations for complex models
C. Traditional AI ignores ethics
D. XAI is only for simple algorithms
Answer: B
Explanation: Traditional AI transparency might involve basic logging, but XAI specifically aims to make opaque, high-performance models understandable to non-experts.
11. Question: What is the purpose of counterfactual explanations in XAI?
A. To predict future events
B. To show what changes in input would lead to a different output
C. To increase model complexity
D. To minimize data usage
Answer: B
Explanation: Counterfactual explanations illustrate alternative scenarios, helping users understand how small changes in inputs could alter predictions, thus providing actionable insights.
12. Question: In XAI, what does model-agnostic mean?
A. The explanation method works only for specific models
B. The explanation method can be applied to any model
C. The model is always accurate
D. The model requires no training
Answer: B
Explanation: Model-agnostic techniques, like LIME, are versatile and can explain decisions from various types of AI models without being tied to a specific architecture.
13. Question: Why is XAI important in regulated industries like finance?
A. To speed up transactions
B. To comply with laws requiring auditable decisions
C. To eliminate human oversight
D. To reduce investment risks entirely
Answer: B
Explanation: Regulated industries need XAI to ensure AI decisions can be audited and justified, meeting legal standards and avoiding biases or errors.
14. Question: Which visualization tool is commonly used in XAI to show feature interactions?
A. Heatmaps
B. Bar charts
C. Line graphs
D. Pie charts
Answer: A
Explanation: Heatmaps in XAI display interactions between features, highlighting how combinations affect predictions and providing a clear visual explanation.
15. Question: What is an example of an inherently interpretable AI model?
A. A convolutional neural network
B. A decision tree
C. A generative adversarial network
D. A recurrent neural network
Answer: B
Explanation: Decision trees are inherently interpretable because their structure allows users to trace the decision path from root to leaf, making explanations intuitive.
16. Question: How can XAI help mitigate bias in AI systems?
A. By ignoring biased data
B. By identifying and explaining the sources of bias in decisions
C. By increasing model complexity
D. By automating bias introduction
Answer: B
Explanation: XAI reveals how features contribute to outcomes, allowing developers to detect and correct biases in the data or model logic.
17. Question: What is the main limitation of rule-based explanations in XAI?
A. They are too flexible
B. They may not capture the nuances of complex models
C. They require no data
D. They always provide perfect accuracy
Answer: B
Explanation: Rule-based explanations simplify decisions but can oversimplify intricate patterns in advanced models, potentially missing subtle interactions.
18. Question: In XAI, what does global explanation refer to?
A. Explaining a single prediction
B. Providing an overview of how the model behaves overall
C. Limiting explanations to local areas
D. Focusing only on errors
Answer: B
Explanation: Global explanations describe the general behavior and patterns of the entire model, helping users understand its overall decision-making process.
19. Question: Why might users prefer XAI in autonomous vehicles?
A. To entertain passengers
B. To understand and verify driving decisions in real-time
C. To reduce fuel consumption
D. To increase vehicle speed
Answer: B
Explanation: XAI in autonomous vehicles provides insights into why certain actions are taken, enhancing safety and user confidence by allowing oversight of critical decisions.
20. Question: What is the role of human-AI interaction in XAI?
A. To replace humans entirely
B. To enable users to question and learn from AI explanations
C. To minimize AI usage
D. To complicate decision processes
Answer: B
Explanation: XAI facilitates human-AI interaction by allowing users to query explanations, fostering collaboration and ensuring decisions align with human values and ethics.
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Part 3: Save time and energy: generate quiz questions with AI technology
Automatically generate questions using AI