Causally Fair Generation with Large Language Models

2026-10-03

AI Summary

This study addresses the challenge that complex causal dependencies in large language model (LLM) generation induce group fairness biases, which traditional statistical methods struggle to identify effectively. To this end, this work proposes CFG, a causal inference-based framework that achieves fair generation through concept extraction, causal graph construction, and selective effect removal. Notably, the framework supports flexible debiasing under non-topological query orders while preserving business-critical paths alongside eliminating discriminatory effects. The primary contributions include formal theoretical guarantees for the debiasing procedure and comprehensive empirical validation across four LLMs and three real-world scenarios. Experimental results demonstrate that the proposed approach significantly enhances the causal fairness of generated content, establishing it as a robust solution for mitigating structural biases in LLM outputs.

Abstract

Large language models (LLMs) are increasingly used to generate, complete, and transform information in settings where their outputs can shape consequential decisions, raising concerns about their impact on demographic disparities. In this context, causal inference provides a principled basis for assessing fairness, because it attributes observed disparities to the mechanisms that generated them, which a purely statistical approach cannot do even with infinite data. In LLM generation, a query may request several causally related variables, each of which is both an outcome of interest and a possible cause of other outputs, and the information supplied in the prompt need not follow a topological or a temporal order. This calls for methods that can analyze and selectively remove disparities from such a flexible generation process. In this paper we introduce Causally Fair Generation with LLMs (CFG, for short). CFG extracts relevant concepts, grounds generation in a reference population and causal diagram, and removes user-selected causal effects. CFG also allows pathways deemed justifiable for the task's utility to be retained, which is known in legal literature as business necessity. Further, we provide formal guarantees for our method when eliminating all discriminatory causal effects in the adapted population model, under appropriate causal assumptions. We evaluate CFG with four LLMs in three real-world settings based on population data and on a synthetic dataset with a known causal ground truth.

Problem

• Large Language Models
• Causal Fairness
• Demographic Disparities
• Causal Inference
• Fair Generation

Innovation

• Causal Fairness
• Large Language Models
• Causal Inference
• Discriminatory Causal Effects
• Business Necessity