# Introduction Language models are now an integral part of our interactions with technology. However, recently discovered hidden biases in their responses. A study analyzed language models and found that they can have tone tendencies, influencing user perception of trust, empathy, and fairness. ## Method Researchers created two synthetic dialogue datasets: one generated from neutral prompts and another explicitly guided to produce positive or negative tones. Using a weak supervision technique with a pre-trained DistilBERT model, they labeled tones and trained several classifiers to detect these patterns. ## Results Models achieved macro F1 scores up to 0.92, showing that tone biases are systematic, measurable, and relevant to designing fair and trustworthy conversational AI. ## Technical context Language models have revolutionized the software industry of voice assistants. However, their impact on biases and emotional impact is still poorly understood. This study opens a new perspective on the need to analyze and control language models to ensure balanced communication.