arXiv:2510.09905v2 Announce Type: replace Abstract: 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 s
The proliferation of personalized AI systems incorporating long-term user memory makes understanding its impact on AI's emotional reasoning capabilities a timely and critical research area.
This research reveals a fundamental ethical and functional challenge in AI personalization, influencing how LLMs interact with users and potentially leading to biased or misinterpreted emotional responses based on stored data.
The understanding that user memory can significantly alter an LLM's emotional reasoning, necessitating more robust evaluation and design principles for personalized AI to ensure fairness and accuracy in interpretation.
- · AI ethicists and researchers
- · Companies developing ethical AI frameworks
- · Users of well-designed, unbiased personalized AI
- · Developers neglecting ethical AI design
- · Users subject to biased AI interpretations
- · Companies relying on superficial personalization
AI models will need improved mechanisms to filter or contextualize user memory to prevent prejudicial emotional reasoning.
Public trust in personalized AI systems could erode if biases in emotional intelligence become widely apparent and unaddressed.
Regulatory bodies may introduce new guidelines or standards specifically addressing how personalized AI models handle and process user-specific contextual data for emotional interpretation.
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Read at arXiv cs.AI