Why does an algorithm trained to debate often amplify existing biases in argumentation?

💡 Explanation

Argumentation algorithms often prioritize persuasiveness metrics rather than objective factual correctness, because the reward function encourages strategies that win debates regardless of truth. Therefore, algorithms may amplify biases already present in data rather than correct them, which results in skewed argumentative strategies, rather than balanced reasoning.

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