ICML 2025

FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for
Enabling Fair LLM-Based Recommender Systems
Arya Fayyazi 1 Mehdi Kamal 1 Massoud Pedram 1
Abstract
We propose FACTER, a fairness-aware frame-
work for LLM-based recommendation systems
that integrates conformal prediction with dynamic
prompt engineering.
By introducing an adap-
tive semantic variance threshold and a violation-
triggered mechanism, FACTER automatically
tightens fairness constraints whenever biased pat-
terns emerge. We further develop an adversar-
ial prompt generator that leverages historical vi-
olations to reduce repeated demographic biases
without retraining the LLM. Empirical results
on MovieLens and Amazon show that FACTER
substantially reduces fairness violations (up to
95.5%) while maintaining strong recommenda-
tion accuracy, revealing semantic variance as a
potent proxy of bias.
1. Introduction
Large Language Models (LLMs) have significantly ad-
vanced natural language processing (NLP), demonstrating
robust generative capabilities across tasks including summa-
rization, dialogue, code completion, and creative composi-
tion. Representative models such as GPT-3 (Brown et al.,
2020), BERT (Devlin et al., 2019), Llama-2 (Touvron et al.,
2023), Llama-3 (Dubey et al., 2024), and Mistral-7B (Jiang
et al., 2023) leverage massive corpora and complex architec-
tures to produce remarkably fluent text, often approaching
or matching human performance in various linguistic bench-
marks. Yet a growing body of work reveals that these models
can inadvertently perpetuate or even amplify biases related
to sensitive attributes such as race, gender, or age (Sheng
et al., 2019; Blodgett et al., 2020; Bary et al., 2021). Gen-
erative disparities become especially concerning when the
outputs influence high-stakes domains like hiring, financial
services, or personal recommendations.
1University of Southern California, Los Angeles, California,
USA. Correspondence to: Arya Fayyazi <afayyazi@usc.e...

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