Sima AIunty: Caste Audit in LLM-Driven Matchmaking
Atharva Naik, Shounok Kar, Varnika Sharma, Ashwin Rajadesingan, and Koustuv Saha

TL;DR
This study audits caste bias in large language models used for matchmaking, revealing that models tend to favor same-caste matches and reproduce traditional caste hierarchies, raising concerns about reinforcing social exclusion.
Contribution
It provides a controlled analysis of caste bias across multiple LLMs in a sensitive social domain, highlighting the reproduction of caste hierarchies in AI evaluations.
Findings
Same-caste matches rated up to 25% higher than inter-caste matches.
Models reproduce traditional caste hierarchies in matchmaking evaluations.
Caste bias persists across different LLM families and social dimensions.
Abstract
Social and personal decisions in relational domains such as matchmaking are deeply entwined with cultural norms and historical hierarchies, and can potentially be shaped by algorithmic and AI-mediated assessments of compatibility, acceptance, and stability. In South Asian contexts, caste remains a central aspect of marital decision-making, yet little is known about how contemporary large language models (LLMs) reproduce or disrupt caste-based stratification in such settings. In this work, we conduct a controlled audit of caste bias in LLM-mediated matchmaking evaluations using real-world matrimonial profiles. We vary caste identity across Brahmin, Kshatriya, Vaishya, Shudra, and Dalit, and income across five buckets, and evaluate five LLM families (GPT, Gemini, Llama, Qwen, and BharatGPT). Models are prompted to assess profiles along dimensions of social acceptance, marital stability,…
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