The Personalization Trap: How User Memory Alters Emotional Reasoning in
  LLMs
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.
68edba56de1fee572713a804
Xi Fang
68edba56de1fee572713a805
Weijie Xu
68edba56de1fee572713a806
Yuchong Zhang
68edba56de1fee572713a807
Stephanie Eckman
68edba56de1fee572713a808
Scott Nickleach
68edba56de1fee572713a809
Chandan K. Reddy
The Personalization Trap: How User Memory Alters Emotional Reasoning in
  LLMs
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.
LLMs exhibit systematic biases in emotional interpretation and support based on user profiles, potentially reinforcing social hierarchies.
large language models emotional intelligence user memory emotional scenarios user profiles systematic biases demographic factors emotion understanding supportive recommendations social hierarchies