[2603.29288] Sima AIunty: Caste Audit in LLM-Driven Matchmaking

Computer Science > Computers and Society arXiv:2603.29288 (cs) [Submitted on 31 Mar 2026] Title:Sima AIunty: Caste Audit in LLM-Driven Matchmaking Authors:Atharva Naik, Shounok Kar, Varnika Sharma, Ashwin Rajadesingan, Koustuv Saha View a PDF of the paper titled Sima AIunty: Caste Audit in LLM-Driven Matchmaking, by Atharva Naik and 4 other authors View PDF HTML (experimental) 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, and cultural compatibility. Our analysis reveals consistent hierarchical patterns across models: same-caste matches are rated most favorably, with average ratings up to 25% higher (on a 10-point scale) than int...