When Surface Form Changes Moderation Decisions: A Paired Study of Code-Mixed Workflow Instability
arXiv:2606.05654v1 Announce Type: cross Abstract: Hate moderation is often evaluated as classification on clean English inputs, but deployed systems must route content to actions such as ALLOW, FLAG, or REVIEW. We study how this workflow changes under code-mixed inputs using a paired evaluation setting where the same underlying content is expressed as clean English and Tamil-English code-mix. Under thresholds tuned on clean English development data, code-mixed inputs produce substantial action instability, with a paired clean- to-code-mix decision flip rate of 0.265. The main workflow effects
The proliferation of AI systems across diverse linguistic and cultural contexts necessitates understanding their real-world performance beyond clean English datasets.
This study highlights a critical vulnerability in AI moderation systems, showing that surface form changes in code-mixed languages can lead to significant decision instability and workflow inefficiencies.
The understanding that AI moderation models trained on English-only data are unreliable and biased when encountering multilingual or code-mixed inputs, requiring re-evaluation of deployment strategies.
- · Multilingual AI research and development
- · Companies specializing in robust, culturally aware AI moderation solutions
- · Platforms deploying English-centric AI moderation globally
- · Users impacted by inconsistent moderation decisions in code-mixed content
Increased investment in multilingual and code-mixed AI training data and model development will occur.
Social platforms and content providers will face pressure to improve moderation accuracy in diverse linguistic environments to maintain user trust and avoid regulatory scrutiny.
The development of truly 'universal' AI moderation systems capable of handling linguistic diversity seamlessly may accelerate, impacting internet governance models.
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Read at arXiv cs.LG