arXiv:2604.08819v2 Announce Type: replace-cross Abstract: Content moderation systems classify images as safe or unsafe but lack spatial grounding and interpretability: they cannot explain what sensitive behavior was detected, who is involved, or where it occurs. We introduce the Sensitive Benchmark (SenBen), the first large-scale scene graph benchmark for sensitive content, comprising 13,999 frames from 157 movies annotated with Visual Genome-style scene graphs (25 object classes, 28 attributes including affective states such as pain, fear, aggression, and distress, 14 predicates) and 16 sensi
The increasing sophistication and scale of AI content moderation systems necessitate better explainability and grounding to address bias and improve accuracy, particularly as regulatory scrutiny intensifies.
This development allows AI content moderation to move beyond simple classification to explainable detection of specific sensitive behaviors, improving transparency, accountability, and the ability to fine-tune moderation policies.
Content moderation systems will transition from opaque black boxes to more interpretable models capable of identifying the 'who, what, and where' of sensitive content, reducing false positives and improving human oversight.
- · Social media platforms
- · Content moderation service providers
- · AI ethics researchers
- · Online safety advocates
- · Platforms with opaque moderation practices
- · Content creators engaging in borderline behavior
AI content moderation systems will become more precise and less prone to over-moderation or under-moderation.
This precision will lead to more nuanced policy enforcement and potentially influence national regulations around digital content.
Improved explainability could eventually enable real-time, personalized content moderation tailored to specific user sensitivities or regional legal frameworks.
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Read at arXiv cs.LG