AI Associate Ethical Considerations of AI Practice Question
A social media platform uses an AI model to automatically detect and remove hate speech. The model uses natural language processing and was trained on public posts. Recently, an internal audit reveals that the model removes posts from minority ethnic groups at a rate 3 times higher than from majority groups, even when the content is similar. The model achieves high precision and recall on the test set. The platform's content moderation team is overwhelmed with appeals. The company wants to maintain a safe environment while being fair. Which approach best addresses both goals?
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
✓
Conduct an audit of the training data to identify gaps, then retrain with more representative data including diverse examples of hate speech and non-hate speech.
A comprehensive audit and retraining with diverse data addresses the bias at the root. Option A gives special treatment that could be seen as unfair. Option C removes moderation, risking harmful content. Option D does not solve the underlying bias.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable the AI moderation and rely solely on user reports.
Why it's wrong here
Without AI moderation, harmful content may proliferate before users report it.
- ✓
Conduct an audit of the training data to identify gaps, then retrain with more representative data including diverse examples of hate speech and non-hate speech.
Why this is correct
This tackles the root cause of bias: underrepresentation of certain groups in training data leads to over-sensitivity.
- ✗
Add more human moderators to review all flagged content from minority groups.
Why it's wrong here
Increasing human review only treats symptoms, not the model's bias, and is costly.
- ✗
Adjust the detection threshold only for minority group posts to reduce flags.
Why it's wrong here
Different thresholds for different groups can be perceived as reverse discrimination and may miss actual hate speech.
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