Google Professional Machine Learning Engineer Question 469
Select 3Google Cloud PlatformYou are a Machine Learning Engineer at an e-commerce company using Google Cloud to develop a recommendation system. During the evaluation phase of the model, you notice that the recommendations for certain demographics are significantly worse than others. In alignment with Google’s Responsible AI practices, which actions should you take to ensure fairness and mitigate bias in your system?
- A
Conduct a fairness evaluation by analyzing model performance across different demographic groups.
- B
Implement adversarial training to improve the model's robustness against biased inputs.
- C
Use counterfactual data to simulate how the model performs for underrepresented groups.
- D
Deploy the model immediately and plan to monitor for bias in production later.
- E
Retrain the model with balanced data that includes more representation from underrepresented groups.
Show answer and explanation
Correct answers: A, C, E
Explanation
To align with Google's Responsible AI practices, it is important to proactively identify, evaluate, and mitigate bias in machine learning models. Conducting fairness evaluations, using counterfactual data for testing, and retraining models with representative data are effective strategies to ensure equitable performance across different demographic groups. Actions like deploying biased models without resolution or using techniques unrelated to fairness (e.g., adversarial training) do not address the core issue of bias and fairness.
- A. Correct.
Conducting a fairness evaluation is a crucial first step to identify and measure the extent of bias in the model's performance across different demographic groups. This aligns with Google's Responsible AI practices.
- B. Incorrect.
Adversarial training is primarily used to improve robustness against adversarial attacks, not for addressing fairness or mitigating bias in the model's output.
- C. Correct.
Using counterfactual data allows you to test how the model would behave in scenarios for underrepresented groups and can help identify fairness issues, which aligns with Responsible AI practices.
- D. Incorrect.
Deploying the model immediately without addressing the identified bias violates Responsible AI practices, as it could lead to unfair outcomes and harm certain user groups.
- E. Correct.
Retraining the model with balanced or augmented data to include underrepresented groups is a recognized technique to address bias and improve fairness in model outcomes.