AIF-C01 Guidelines for Responsible AI Practice Question
A government agency is deploying an AI system to detect fraudulent benefit claims. The system uses a neural network trained on historical claims data, which includes a disproportionate number of false positives from a particular ethnic group due to historical over-policing. The agency must ensure the system does not perpetuate discrimination. They have a rigorous testing procedure but limited budget. The project lead wants to balance fairness with detection performance. Which combination of steps should they prioritize?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Rebalance the training data to have equal representation across groups and evaluate using a fairness metric like equal opportunity
The most effective approach is to rebalance the training data to be more representative and to use a fairness metric, such as equal opportunity, during evaluation. This directly addresses the data bias and quantifies fairness. Excluding race features may still leave proxies. Using a simpler model may not eliminate bias if data is biased. Post-hoc explanations help understand bias but do not fix it.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the race feature from the model and rely on performance metrics alone
Why it's wrong here
Removing race does not remove proxies, and performance metrics alone may hide bias.
- ✗
Replace the neural network with a logistic regression model retrained on the same data
Why it's wrong here
Replacing the neural network with logistic regression does not address the root cause of the bias, which is encoded in the training data itself through historical over-policing; logistic regression will simply learn the same skewed decision boundaries from that data, perpetuating the disproportionate false positives. This option is tempting because logistic regression is often chosen for its interpretability and lower computational cost, making it a correct choice when the goal is to audit a model’s decision logic under a limited budget, but here the data, not the model architecture, is the source of discrimination.
- ✓
Rebalance the training data to have equal representation across groups and evaluate using a fairness metric like equal opportunity
Why this is correct
Rebalancing data and using fairness metrics directly mitigate bias and measure progress.
- ✗
Use a post-hoc explainability tool to identify biased predictions and manually override them
Why it's wrong here
Manual override is not scalable and does not fix the underlying model.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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