The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs
This work addresses a critical problem for AI developers and users by revealing that personalization mechanisms can inadvertently reinforce social inequalities in emotional intelligence tasks.
The study investigated how user memory in personalized AI systems affects emotional reasoning in large language models (LLMs), finding that identical scenarios with different user profiles led to systematic biases where advantaged profiles received more accurate emotional interpretations, with significant disparities across demographic factors.
When an AI assistant remembers that Sarah is a single mother working two jobs, does it interpret her stress differently than if she were a wealthy executive? As personalized AI systems increasingly incorporate long-term user memory, understanding how this memory shapes emotional reasoning is critical. We investigate how user memory affects emotional intelligence in large language models (LLMs) by evaluating 15 models on human validated emotional intelligence tests. We find that identical scenarios paired with different user profiles produce systematically divergent emotional interpretations. Across validated user independent emotional scenarios and diverse user profiles, systematic biases emerged in several high-performing LLMs where advantaged profiles received more accurate emotional interpretations. Moreover, LLMs demonstrate significant disparities across demographic factors in emotion understanding and supportive recommendations tasks, indicating that personalization mechanisms can embed social hierarchies into models emotional reasoning. These results highlight a key challenge for memory enhanced AI: systems designed for personalization may inadvertently reinforce social inequalities.