Which approach best mitigates biases in AI-generated text?

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Mitigating biases in AI-generated text relies on combining rigorous verification with human judgment and cross-checking. Verifying with trusted sources helps ensure outputs reflect credible, evidence-based information rather than biased or false claims. Involving human review adds contextual awareness, checks for sensitive or inappropriate language, and catches subtleties that automated checks often miss. Cross-checking information against multiple sources helps identify inconsistencies and prevents reliance on a single dataset. This approach directly addresses data quality and model behavior, reducing the risk of biased or misleading content being produced or amplified. Increasing dataset size without evaluating data quality can amplify biases, relying solely on automated metrics may miss nuanced biases, and skipping validation steps lets problematic outputs slip through.