If AI systems trained on ASL gloss are shipping "confidently wrong" translations, what does good training data actually look like — and who validates it before it ships?
Gloss isn't ASL. Strip away facial grammar, classifiers, spatial reference, role shift, and cultural register, and you get a model that's fluent in a language that doesn't exist — and confident while it's wrong. "Good data" is more than hours of video. It's validated, diverse, context-rich, and reviewed by Deaf ASL experts with authority to reject bad output. What should that pipeline actually require — and who holds the pen?
Prompts to get you thinking
- What's missing from most sign language training datasets right now?
- Who should have validation authority — and what does "authority" mean when vendors control the ship date?
- Have you seen a system confidently produce wrong ASL? What did it get wrong, and what should've caught it?
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