How to Answer Content Moderation ML Challenges at X (Twitter)

To design a machine learning system for identifying harmful content on X (Twitter), I would start by defining categories such as hate speech, harassment, and misinformation. I would gather a diverse dataset, incorporating user reports and expert annotations to label examples accurately. For modeling, I would consider using transformer-based architectures like BERT for their strong contextual understanding. Evaluation would involve metrics like precision, recall, and F1-score, ensuring we maintain a balance between precision and recall to reduce false positives. Additionally, addressing potential biases by regularly auditing the model and employing techniques like adversarial debiasing would be crucial to maintain fairness in moderation.