AIF-C01 exam dumps

AIF-C01 practice question 162 of 231

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

AIF-C01 Question 162

Select 3

A company is using an AI model to screen job applications and recommend candidates for interviews. During an audit, they discover that the model is consistently favoring candidates from a certain demographic while disadvantaging others. Which aspects of responsible AI should the company prioritize to address this issue?

  1. A

    Fairness

  2. B

    Inclusivity

  3. C

    Robustness

  4. D

    Safety

  5. E

    Bias

Show answer and explanation

Correct answers: A, B, E

Explanation

To fix the issue of favoring certain demographics in hiring decisions, the company must focus on fairness, inclusivity, and addressing bias. Fairness ensures equitable treatment, inclusivity ensures diverse representation in the model's design and training, and mitigating bias ensures decisions are objective and impartial. Robustness and safety, while important in other contexts, are not directly relevant to this specific issue.

  • A. Correct.

    Fairness: Correct. Fairness ensures that the model's decisions are equitable and do not systematically disadvantage certain groups, making this a critical aspect to address the issue.

  • B. Correct.

    Inclusivity: Correct. Inclusivity involves designing AI systems that represent and consider diverse demographics, which is essential to mitigate this bias.

  • C. Incorrect.

    Robustness: Incorrect. While robustness ensures the model performs well under various conditions, it does not directly address the demographic bias in this scenario.

  • D. Incorrect.

    Safety: Incorrect. Safety focuses on avoiding harm caused by the AI system (e.g., physical or operational safety), which is unrelated to demographic bias in hiring decisions.

  • E. Correct.

    Bias: Correct. Addressing bias is fundamental in this scenario, as the model's behavior indicates a skewed decision-making process that needs correction.

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