Quiz: The Explainable Artificial Intelligence Challenge
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What is an advantage of Explainable AI?
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Increases transparency
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Increases model complexity
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Decreases computational efficiency
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None of the above
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Quiz: The Explainable Artificial Intelligence Challenge" offers a well-rounded exploration of the Explainable AI (XAI) concept. The Explainable Artificial Intelligence Quiz initiates with an understanding of what XAI means, delving into its significance in AI accountability, trust, and robustness. It extends into prominent techniques like LIME (Local Interpretable Model-Agnostic Explanations) and their goals, exploring more intricate concepts such as counterfactual explanations.
The quiz also probes into prevalent challenges in XAI, including the explainability-accuracy trade-off. Finally, it touches upon methods like Shapley value to estimate feature impacts, the advantages, and disadvantages of XAI, and its application in fields like medical diagnostics and credit risk assessment.
Quiz Preview
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Which real-world application can benefit from Explainable AI?
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Medical diagnostics
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Autonomous vehicles
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Credit risk assessment
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All of the above
Correct Answer
A. All of the aboveExplanation
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.Rate this question:
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- 3.
Why is Explainable AI important?
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To hold AI accountable
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To improve trust in AI systems
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To improve the robustness of AI
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All of the above
Correct Answer
A. All of the aboveExplanation
It helps characterize model accuracy, fairness, transparency, and outcomes in AI-powered decision making.Rate this question:
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- 4.
How is Shapley value used in Explainable AI?
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To estimate the impact of features on prediction
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To estimate the accuracy of the model
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To estimate the computational complexity
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None of the above
Correct Answer
A. To estimate the impact of features on predictionExplanation
The Shapley value is the average marginal contribution of a feature value across all possible coalitionsRate this question:
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- 5.
What is Explainable Artificial Intelligence?
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AI that can explain its own processes
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AI that can learn without supervision
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AI that can interact with humans
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AI that can make decisions independently
Correct Answer
A. AI that can explain its own processesExplanation
Explainable AI refers to systems that can clearly articulate their internal processes and decision-making.Rate this question:
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- 6.
What does LIME stand for in Explainable AI?
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Local Interpretable Model-Agnostic Explanations
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Low Input Model-Agnostic Explanations
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Linear Interpolated Model-Agnostic Explanations
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None of the above
Correct Answer
A. Local Interpretable Model-Agnostic ExplanationsExplanation
The explainable AI method LIME (Local Interpretable Model-agnostic Explanations) helps to illuminate a machine learning model and to make its predictions individually comprehensible.Rate this question:
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- 7.
What is the goal of LIME?
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To explain individual predictions
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To improve the accuracy of AI models
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To improve the efficiency of AI models
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None of the above
Correct Answer
A. To explain individual predictionsExplanation
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.Rate this question:
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- 8.
Which of the following is a challenge for Explainable AI?
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Explainability-accuracy trade-off
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Lack of standardized evaluation metrics
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Complexity of interpretability
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All of the above
Correct Answer
A. All of the aboveExplanation
Explainable AI faces several challenges including the trade-off between accuracy and explainability, lack of standard metrics, and interpretability complexity.Rate this question:
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- 9.
Which method in Explainable AI focuses on inputs that would change the output classification?
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Feature Importance
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Partial Dependence Plots
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Counterfactual Explanations
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None of the above
Correct Answer
A. Counterfactual ExplanationsExplanation
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 variablesRate this question:
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- 10.
What is the main disadvantage of Explainable AI?
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It requires more data
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It is computationally intensive
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It reduces the accuracy of AI models
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All of the above
Correct Answer
A. It is computationally intensiveExplanation
The main disadvantage of Explainable AI is that it can be computationally intensive, especially for complex models.Rate this question:
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Quiz Review Timeline (Updated): Aug 3, 2023 +
Our quizzes are rigorously reviewed, monitored and continuously updated by our expert board to maintain accuracy, relevance, and timeliness.
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Current Version
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Aug 03, 2023Quiz Edited by
ProProfs Editorial Team -
Aug 02, 2023Quiz Created by
Madhurima Kashyap
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