EquiCaste: Auditing Caste Bias in Large Language Models via Paired Communication Research

The EquiCaste project focuses on auditing caste bias in large language models (LLMs), using paired communication research methods derived from sociology to provide a rigorous practical example for AI fairness assessment. This study not only reveals implicit caste bias in LLMs but also provides references for model improvement, policy formulation, and user empowerment, representing an important advancement in AI ethics and fairness research.

LLM training data comes from the internet and inevitably carries biases from human society. When used in high-risk scenarios such as education and recruitment, these biases may lead to social injustice. However, identifying and quantifying biases is challenging, especially for biases like caste that have deep historical and cultural roots—simple assessments struggle to capture deep structural patterns, requiring fine-grained contextualized methods.

Paired communication research observes response differences by controlling variables and only changing the target feature (caste identity). EquiCaste innovatively applies this to LLM auditing: designing paired prompt templates, controlling variables, comparing output differences, and quantifying the degree of bias. This method has the advantages of causal inference capability, ecological validity, and fine-grained analysis, which is superior to simple vocabulary detection.

Caste bias has unique complexities: implicit encoding (via indirect clues like names and regions), intersectionality (intertwined with class, etc.), context dependence (the meaning of the same expression changes with context), and historical continuity (persistence of implicit biases), which increase the difficulty of auditing.

The EquiCaste technical framework may include: a prompt engineering module (designing paired prompts with caste clues), a response analysis module (content/language feature/decision simulation analysis), and a statistical evaluation framework (effect size calculation, significance testing, multiple comparison correction, etc.).

Social significance: revealing implicit biases, guiding model improvement, providing a basis for policies, and empowering users. Future directions: multilingual expansion, dynamic monitoring, intervention strategy research, and interdisciplinary collaboration.

Developers should attach importance to bias auditing (especially in the target cultural context), adopt rigorous methods (such as paired communication), disclose audit results, and continuously monitor and improve to ensure model fairness.