Odd One Out Question 3 - CAT 2020
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.
Step-by-Step Explanation
Correct Answer: 3 Explanation: Statements (2), (4), (1), and (5) form a logical paragraph. They discuss how algorithms are used to detect hate speech and then explain the problems and limitations of these algorithms. Statement (3) is the odd one out because it does not fit with the main discussion about hate speech detection and its challenges. Therefore, Statement (3) is the correct answer.Odd One Out Shortcuts & Tricks
Are Odd One Out Questions Worth Attempting in CAT?
No negative marking: Odd One Out questions are generally TITA questions, making them worth considering even when you are not completely certain.
Relatively quick to attempt: If you identify strong sentence connections early, the odd sentence can often be isolated without arranging the entire paragraph.
Good scoring opportunity: Familiarity with paragraph structure and logical flow can make these questions rewarding.
But don’t overinvest time: If no clear connections emerge, move ahead and return to the question later.
Bottom Line: Attempt Odd One Out questions strategically—they can add valuable marks without the penalty associated with incorrect MCQ answers.