Veracity labels flip the emotional tone of language models but leave racial disparities intact

Research evaluating four contemporary language models: GPT-5.4 mini, NVIDIA Nemotron 3 Nano, Llama 4 Scout, and Claude Haiku 4.5, reveals that while applying veracity labels to fact-checked headlines shifts simulated emotional tone across multiple dimensions, it fails to eliminate underlying racial disparities across demographic personas.

This research demonstrates that factual correctness and demographic fairness operate through distinct mechanisms within language models. Deploying veracity indicators successfully alters emotional posture but leaves identity-based disparities unresolved, meaning technical truthfulness cannot serve as a proxy for equitable treatment in generative AI deployments.

Veracity labels significantly shift the emotional tone of language models across simulated affective dimensions including mood, anger, fear, confidence, perceived control, and mental state.

Forward consideration, not a verified fact.