Sima AIunty: Caste Audit in LLM-Driven Matchmaking

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 inter-caste matches, which are further ordered according to traditional caste hierarchy. These findings highlight how existing caste hierarchies are reproduced in LLM decision-making and underscore the need for culturally grounded evaluation and intervention strategies in AI systems deployed in socially sensitive domains, where such systems risk reinforcing historical forms of exclusion.

Promoting Constructive Deliberation: Reframing for Receptiveness

To promote constructive discussion of controversial topics online, we propose automatic reframing of disagreeing responses to signal receptiveness while preserving meaning. Drawing on research from psychology, communications, and linguistics, we identify six strategies for reframing. We automatically reframe replies according to each strategy, using a dataset of Reddit comments and replies. Through human-centered experiments, we find that the replies generated with our framework are perceived to be significantly more receptive than the original replies, as well as a generic receptiveness baseline. We analyze and discuss the implications of our results and highlight applications to content moderation. Overall, we illustrate how transforming receptiveness, a particular social science construct, into a computational framework, can make LLM generations more aligned with human perceptions.