Failure Modes of Machine Learning–Based Content Moderation Systems: An Analysis of False Positives, False Negatives, Coordinated Abuse, and Adversarial Synthetic Media

The attached file is a research paper titled "Failure Modes of Machine Learning-Based Content Moderation Systems: An Analysis of False Positives, False Negatives, Coordinated Abuse, and Adversarial Synthetic Media." This paper examines systematic failures in deployed ML moderation systems despite accuracy improvements. The paper analyzes issues like false positives/negatives, coordinated abuse, and deepfakes, framing moderation as a policy-driven adversarial challenge rather than simple classification. It introduces a practitioner taxonomy for failures due to distribution shifts and ambiguity, proposing mitigations such as human-in-the-loop processes and risk-tiered enforcement. Limitations of accuracy metrics are critiqued, advocating policy-aware frameworks for Trust & Safety.