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 jumbled sentences relate to the topic of bias and hate speech detection in machine learning. Let's analyze them to determine which sentence doesn't fit with the others.

1. Machine learning models are prone to learning human-like biases from the training data that feeds these algorithms.
2. Hate speech detection is part of the on-going effort against oppressive and abusive language on social media.
3. The current automatic detection models miss out on something vital: context.
4. It uses complex algorithms to flag racist or violent speech faster and better than human beings alone.
5. 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.

The sentences discuss machine learning and hate speech detection, focusing on the challenges and efforts to manage biases. Sentences 1, 2, 4, and 5 contribute to this theme:

• Sentence 1 introduces the concept of biases in machine learning models.
• Sentence 2 relates to the effort against hate speech detection as part of a broader context.
• Sentence 4 highlights the effectiveness of algorithms in detecting abusive language.
• Sentence 5 explains the challenge of identifying offensive language due to biased training data.

The odd one out is sentence 3, as it shifts focus to the lack of context in detection models, which is a different issue than the collective theme. Thus, the number of the sentence that doesn't fit is: 3.