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Grokking the Machine Learning Interview

Metrics

Explore key evaluation metrics critical to hate speech detection systems, including precision, recall, and F1-score. Understand the social and ethical implications of metric choices, how to handle class imbalance, and the importance of human-in-the-loop strategies. This lesson prepares you to discuss metrics thoughtfully in ML system design interviews, emphasizing real-world trade-offs and platform policies.

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Why metrics matter

In hate speech detection, metrics are not just numbers; they define what the platform considers acceptable harm. A model that blocks too much speech risks censorship and user backlash. A model that misses harmful content risks real-world harm, legal scrutiny, and reputational damage. Unlike many ML problems, errors here have social, ethical, and legal consequences, not just financial ones.

This makes metric selection a value judgment, not merely a technical choice.

A common interview trap is to treat hate speech detection as a normal text classification problem. Strong candidates immediately recognize that label ambiguity, class imbalance, and asymmetric costs are the primary challenges in this domain.

Informative note: Hate speech datasets often contain less than 5% hateful content, and sometimes even below 1%. This imbalance heavily influences which metrics are meaningful.

Dataset imbalance in content moderation
Dataset imbalance in content moderation

Confusion matrix

All evaluation metrics originate from the confusion matrix, which helps reason about the types of mistakes, not just the number of them.

Confusion matrix with real-world consequences
Confusion matrix with real-world consequences

Each error has a very different implication:

  • True Positive (TP)
    This is content that is actually hate speech, and the model correctly predicts it as hate. These are “good catches”; the system successfully prevents harmful content.

  • False Negative (FN)
    This is content that is actually hate speech, but the model predicts it as non-hate. This is dangerous because harmful content can slip through and reach users.

  • False Positive (FP)
    This is content that is not hate speech, but the model predicts it as hate. This leads to over-moderation, user frustration, concerns about censorship, and a loss of trust.

  • True Negative (TN)
    This is content ...