Intersectional Sycophancy: How Perceived User Demographics Shape False Validation in Large Language Models
Large language models exhibit sycophantic tendencies, but whether this behavior varies systematically with perceived user demographics is underexplored. Inspired by intersectionality (overlapping identities produce compounded effects), we probe whether frontier models conditionally exhibit sycophancy. Across 768 multi-turn conversations spanning 128 personas (varying race, age, gender, confidence) and three domains (mathematics, philosophy, conspiracy theories), we find that sycophancy varies sharply with target model and domain, and emerges from combinations of perceived user traits rather than any single dimension. GPT-5-nano scores far higher than Claude Haiku 4.5 (average sycophancy s...
| Authors | Benjamin Maltbie, Shivam Raval |
|---|---|
| Primary category | cs.AI |
| Published | 13 April 2026 |
| Last updated at source | 4 May 2026 |
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