Quiz: The Explainable Artificial Intelligence Challenge

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| By Madhurima Kashyap
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Madhurima Kashyap
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Quizzes Created: 39 | Total Attempts: 11,612
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  • 1/10 Questions

    What is an advantage of Explainable AI?

    • Increases transparency
    • Increases model complexity
    • Decreases computational efficiency
    • None of the above
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About This Quiz

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: The Explainable Artificial Intelligence Challenge - Quiz

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  • 2. 

    Which real-world application can benefit from Explainable AI?

    • Medical diagnostics

    • Autonomous vehicles

    • Credit risk assessment

    • All of the above

    Correct Answer
    A. All of the above
    Explanation
    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.

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  • 3. 

    Why is Explainable AI important?

    • To hold AI accountable

    • To improve trust in AI systems

    • To improve the robustness of AI

    • All of the above

    Correct Answer
    A. All of the above
    Explanation
    It helps characterize model accuracy, fairness, transparency, and outcomes in AI-powered decision making.

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  • 4. 

    Which of the following is a challenge for Explainable AI?

    • Explainability-accuracy trade-off

    • Lack of standardized evaluation metrics

    • Complexity of interpretability

    • All of the above

    Correct Answer
    A. All of the above
    Explanation
    Explainable AI faces several challenges including the trade-off between accuracy and explainability, lack of standard metrics, and interpretability complexity.

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  • 5. 

    How is Shapley value used in Explainable AI?

    • To estimate the impact of features on prediction

    • To estimate the accuracy of the model

    • To estimate the computational complexity

    • None of the above

    Correct Answer
    A. To estimate the impact of features on prediction
    Explanation
    The Shapley value is the average marginal contribution of a feature value across all possible coalitions

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  • 6. 

    What does LIME stand for in Explainable AI?

    • Local Interpretable Model-Agnostic Explanations

    • Low Input Model-Agnostic Explanations

    • Linear Interpolated Model-Agnostic Explanations

    • None of the above

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

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  • 7. 

    What is the goal of LIME?

    • To explain individual predictions

    • To improve the accuracy of AI models

    • To improve the efficiency of AI models

    • None of the above

    Correct Answer
    A. To explain individual predictions
    Explanation
    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.

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  • 8. 

    What is Explainable Artificial Intelligence?

    • AI that can explain its own processes

    • AI that can learn without supervision

    • AI that can interact with humans

    • AI that can make decisions independently

    Correct Answer
    A. AI that can explain its own processes
    Explanation
    Explainable AI refers to systems that can clearly articulate their internal processes and decision-making.

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  • 9. 

    Which method in Explainable AI focuses on inputs that would change the output classification?

    • Feature Importance

    • Partial Dependence Plots

    • Counterfactual Explanations

    • None of the above

    Correct Answer
    A. Counterfactual Explanations
    Explanation
    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

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  • 10. 

    What is the main disadvantage of Explainable AI?

    • It requires more data

    • It is computationally intensive

    • It reduces the accuracy of AI models

    • All of the above

    Correct Answer
    A. It is computationally intensive
    Explanation
    The main disadvantage of Explainable AI is that it can be computationally intensive, especially for complex models.

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Quiz Review Timeline (Updated): Aug 3, 2023 +

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  • Current Version
  • Aug 03, 2023
    Quiz Edited by
    ProProfs Editorial Team
  • Aug 02, 2023
    Quiz Created by
    Madhurima Kashyap
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