Responsible AI: Mitigating Bias in Generative Models

A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteria. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers. Which actions should the company take to meet these requirements? (Choose two.)

Detect imbalances or disparities in the data. Source Reference Answer
Ensure that the model runs frequently.
Evaluate the model's behavior so that the company can provide transparency to stakeholders. Source Reference Answer
Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.
Ensure that the model's inference time is within the accepted limits.

Community Votes

100%

100% of anonymous learners picked answer AC. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

The exam tests the ability to distinguish between technical performance metrics and ethical safeguards, specifically highlighting that addressing data disparity and ensuring transparency are critical for fairness.

This question focuses on implementing Responsible AI principles to minimize bias in generative AI solutions. The community consensus confirms that detecting data imbalances and evaluating model behavior for transparency are the key actions required.

Candidates often select options related to model accuracy or speed (like ROUGE or inference time) because they sound like standard ML optimization goals, failing to recognize these do not address the specific requirement of minimizing bias.

Community Discussion (4 comments)

kopper2019 👍 1

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

Jessiii 👍 1 Selected: 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.

dspd 👍 1 Selected: 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

jove 👍 4 Selected: AC

A & C looks correct

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Expert Analysis

Why the Answer Is Correct

To meet responsible AI requirements, organizations must first identify sources of bias in the training data. Detecting imbalances or disparities (Option A) allows developers to correct skewed datasets before training, ensuring fair representation. Additionally, evaluating the model's behavior (Option C) is essential for providing transparency to stakeholders, which builds trust and allows for ongoing monitoring of potential discriminatory outcomes.

Why the Other Options Are Wrong

Options B and E focus on operational efficiency (frequency and latency), which are unrelated to ethical fairness or bias mitigation. Option D suggests using ROUGE to ensure 100% accuracy; however, ROUGE measures text similarity in summarization tasks, does not guarantee 100% accuracy (an unrealistic goal anyway), and does not measure bias or fairness.

Community Comment Notes

The community unanimously agrees on AC, with multiple comments reinforcing that bias originates from data issues. Comment [2] correctly highlights that detecting imbalances ensures fair treatment across customer segments, while Comment [3] notes this aligns with responsible AI development frameworks.

Official Reference

https://aws.amazon.com/responsible-ai/

Exam Strategy

When a question mentions 'responsible AI,' 'bias,' or 'fairness,' immediately look for answers involving data auditing, diversity checks, and transparency/monitoring. Avoid selecting options focused solely on speed, cost, or raw accuracy metrics unless explicitly linked to fairness.

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