Examtopics

コメント(4)

• 正解だと思う選択肢: AC

  A & C looks correct

  👍 4

  jove2024/11/05
• 正解だと思う選択肢: AC

  A. Detect imbalances or disparities in the data C. Evaluate the model's behavior so that the company can provide transparency to stakeholders

  Why:

  Detecting imbalances or disparities in the data is crucial because: It helps identify potential bias in training data before it affects model decisions It ensures fair treatment across different customer segments It aligns with responsible AI development practices Evaluating model behavior for transparency is important because: It allows stakeholders to understand how decisions are made It helps demonstrate compliance with fair lending regulations It enables the company to justify decisions to customers and regulators

  below incorrect because:

  B (frequent model runs) doesn't address bias or responsible AI D (ROUGE technique) is for text summarization evaluation, not lending decisions E (inference time) is about performance, not fairness or responsibility

  👍 1

  dspd2025/01/29
• 正解だと思う選択肢: AC

  A. Detect imbalances or disparities in the data: Bias often originates from imbalanced or biased data. By detecting and addressing any imbalances or disparities in the data (such as certain groups being overrepresented or underrepresented), the company can ensure that the model treats all applicants fairly, regardless of their background. This helps to minimize potential bias that could negatively affect certain customers.

  C. Evaluate the model's behavior so that the company can provide transparency to stakeholders: Evaluating the model’s behavior is crucial for responsible AI usage. By assessing how the model performs across different customer groups, the company can ensure that the model is not inadvertently discriminating or providing unfair treatment. Transparency about the model's decision-making process also builds trust with stakeholders and customers.

  👍 1

  Jessiii2025/02/11