Question

Five jumbled up sentences, related to a topic, are given below. Four of them can be put together to form a coherent paragraph. Identify the odd one out and key in the number of the sentence as your answer:

• A. Machine learning models are prone to learning human-like biases from the training data that feeds these algorithms.
• B. Hate speech detection is part of the on-going effort against oppressive and abusive language on social media.
• C. The current automatic detection models miss out on something vital: context.
• D. It uses complex algorithms to flag racist or violent speech faster and better than human beings alone.
• E. For instance, algorithms struggle to determine if group identifiers like "gay" or "black" are used in offensive or prejudiced ways because they're trained on imbalanced datasets with unusually high rates of hate speech.

Answer 1

The Correct Option is C

The five reordered sentences address bias and hate speech detection within machine learning. Analysis reveals the outlier sentence.

1. Machine learning models can absorb human biases from their training data.
2. Hate speech detection is a key component of combating harmful language online.
3. Current automated detection models fail to account for context.
4. These models employ sophisticated algorithms to identify and flag racist or violent language with greater speed and accuracy than humans.
5. For example, algorithms struggle to discern the offensive use of group identifiers like "gay" or "black" because their training data is imbalanced, containing disproportionately high rates of hate speech.

The sentences collectively concern machine learning and hate speech detection, specifically addressing the challenges and efforts to mitigate biases. Sentences 1, 2, 4, and 5 support this central theme:

• Sentence 1 establishes the issue of biases learned by machine learning models.
• Sentence 2 frames hate speech detection within the broader context of combating abuse.
• Sentence 4 emphasizes the efficiency of algorithms in identifying abusive language.
• Sentence 5 details a specific challenge: identifying offensive language due to biased training datasets.

Sentence 3 is the outlier, as it introduces a distinct issue: the absence of context in detection models, deviating from the primary theme. Therefore, sentence number 3 does not fit. The sentence number that does not fit is: 3.