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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #170

Your organization manages an online message board. A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flag

The correct answer is D. Raise the threshold for comments to be considered toxic or harmful.. To address a high false positive rate for benign comments from underrepresented religious groups with limited resources, you should raise the classification threshold for comments to be considered toxic.

Submitted by amina.ke· Apr 18, 2026Monitoring, optimizing, and maintaining ML solutions

Question

Your organization manages an online message board. A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive. Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

Options

  • AAdd synthetic training data where those phrases are used in non-toxic ways.
  • BRemove the model and replace it with human moderation.
  • CReplace your model with a different text classifier.
  • DRaise the threshold for comments to be considered toxic or harmful.

How the community answered

(61 responses)
  • A
    3% (2)
  • B
    5% (3)
  • C
    11% (7)
  • D
    80% (49)

Why each option

To address a high false positive rate for benign comments from underrepresented religious groups with limited resources, you should raise the classification threshold for comments to be considered toxic.

AAdd synthetic training data where those phrases are used in non-toxic ways.

Adding synthetic training data requires development effort for data generation and integration, which may not be feasible for an overextended team with a limited budget.

BRemove the model and replace it with human moderation.

Replacing the model with human moderation would incur significant operational costs and discard the existing ML investment, which is contrary to having a limited budget.

CReplace your model with a different text classifier.

Replacing the model with a different text classifier demands substantial research, development, and deployment efforts, which are not suitable for a team that is already overextended and has a limited budget.

DRaise the threshold for comments to be considered toxic or harmful.Correct

Raising the threshold for comments to be classified as toxic directly reduces the model's sensitivity, lowering the false positive rate for benign comments without requiring extensive retraining or new data collection. This is a quick, cost-effective intervention, aligning with the constraints of a limited budget and overextended team.

Concept tested: Model bias mitigation with classification thresholds

Source: https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall

Topics

#Model Monitoring#Bias Mitigation#Classification Thresholds#MLOps

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