arXiv:2606.05748v1 Announce Type: cross Abstract: Global-scale video moderation faces a dual challenge: the need for fine-grained multi-modal reasoning and the demand for interpretable outputs to support downstream enforcement. Traditional moderation systems often rely on fragmented black-box classifiers that are difficult to maintain and lack transparency. In this paper, we present UNIVID, a UNIfied VIsion-language model for video moDeration. Unlike standard classification models, UNIVID generates policy-aware captions that serve as an interpretable intermediate representation, enabling human
The proliferation of video content across platforms, coupled with increasing regulatory scrutiny and the capabilities of advanced AI, makes automated and interpretable moderation a critical need.
This development addresses the scalability challenge of content moderation for video, moving towards more transparent and efficient systems, which is crucial for public platforms and regulatory compliance.
Content moderation shifts from opaque, fragmented systems to unified, AI-driven models that provide interpretable outputs, enabling faster and more consistent enforcement.
- · Social media platforms
- · Content moderation service providers
- · AI developers
- · Regulators
- · Manual content moderation workforces
- · Platforms with fragmented legacy moderation systems
More efficient and scalable video content moderation systems are deployed, reducing human workload and improving content hygiene.
The interpretability of AI moderation outputs leads to greater trust in platform safety measures and potentially more consistent policy application across diverse content types.
The development of highly capable and transparent moderation AI could reduce legislative pressure on platforms, shifting some responsibility towards the AI's capabilities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL