The challenge of hallucination you highlight is exacerbated in voice-first interfaces. When a user asks 'mujhe bukhar hai' (I have a fever) in their mother tongue, they need accuracy, not plausible invention. There are no visual cues to flag uncertainty in a spoken response.
This makes data provenance and confidence scoring even more critical.
I focused mostly on text-based AI hallucinations, but you opened up my mental model further. You are right, voice makes it trickier because users lose visual trust signals and confidence can be mistaken for correctness.
The multilingual example makes this even more real. Provenance + confidence scoring feels critical in these use cases. Do you think voice assistants should say “I’m not certain” or “this information comes from medical guidelines” instead of optimizing purely for smooth conversational flow in the future? Or something else?
Thanks for sharing this perspective!
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The challenge of hallucination you highlight is exacerbated in voice-first interfaces. When a user asks 'mujhe bukhar hai' (I have a fever) in their mother tongue, they need accuracy, not plausible invention. There are no visual cues to flag uncertainty in a spoken response.
This makes data provenance and confidence scoring even more critical.
I focused mostly on text-based AI hallucinations, but you opened up my mental model further. You are right, voice makes it trickier because users lose visual trust signals and confidence can be mistaken for correctness.
The multilingual example makes this even more real. Provenance + confidence scoring feels critical in these use cases. Do you think voice assistants should say “I’m not certain” or “this information comes from medical guidelines” instead of optimizing purely for smooth conversational flow in the future? Or something else?
Thanks for sharing this perspective!