Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 49 discussion

The correct answers are A and C: A: Detect imbalances or disparities in the data C: Evaluate the model's behavior so that the company can provide transparency to stakeholders Let's analyze why these are correct: Detect imbalances or disparities in the data (A): Essential for identifying potential sources of bias in training data Helps ensure fair representation across different customer groups Allows for correction of data bias before model training Critical for responsible AI development in financial services Evaluate model's behavior for transparency (C): Enables stakeholder understanding of model decisions Helps identify potential discriminatory patterns Supports regulatory compliance Essential for maintaining accountability

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.

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

A & C looks correct