AIF-C01 exam dumps

AIF-C01 practice question 196 of 231

AWS Certified AI Practitioner. Associate level, Amazon Web Services. Free question with the correct answer and a full explanation.

AIF-C01 Question 196

Select 3

A company is using a machine learning model to evaluate loan applications. The team wants to ensure that the model is explainable to non-technical users, such as loan applicants and customer service representatives. Which of the following principles of human-centered design should the team follow to achieve explainable AI?

  1. A

    Provide clear and simple explanations of the model's predictions in a way that non-technical users can understand.

  2. B

    Ensure the explanation includes only technical terms and mathematical equations to maintain accuracy.

  3. C

    Design the explanation system to highlight the most influential factors that contributed to the model's decision.

  4. D

    Allow users to interact with the system to understand how different inputs could affect the predictions.

  5. E

    Focus solely on optimizing the model's accuracy and disregard user feedback about interpretability.

Show answer and explanation

Correct answers: A, C, D

Explanation

Human-centered design for explainable AI focuses on creating systems that are transparent, interpretable, and accessible to end users. Clear explanations, highlighting key decision factors, and enabling interactivity are essential to ensure non-technical users can trust and understand AI-driven decisions. Neglecting these principles in favor of purely technical accuracy or complexity undermines the model's usability and reliability for the target audience.

  • A. Correct.

    Providing clear and simple explanations tailored to non-technical users aligns with human-centered design principles and helps build trust in the AI system.

  • B. Incorrect.

    Using only technical terms and mathematical equations alienates non-technical users and violates the principle of designing for the audience's needs.

  • C. Correct.

    Highlighting the most influential factors in the model's decision-making process improves transparency and helps users understand why a decision was made.

  • D. Correct.

    Interactive explanations allow users to explore the model's behavior, making it easier for them to grasp how inputs influence outcomes, a key principle of explainable AI.

  • E. Incorrect.

    Focusing solely on accuracy while ignoring the interpretability for end users does not align with human-centered design principles.

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