# FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems

arXiv: https://arxiv.org/abs/2502.02966 · HTML: https://mlsanity.com/paper/2502.02966/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2025.

## Built on by (most cited first, 6 of 6)

- [FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repair](https://mlsanity.com/paper/2504.07395/) (2025)
- [MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Prediction Filtering](https://mlsanity.com/paper/2506.13755/) (2025)
- [Proof-of-Perception: Certified Tool-Using Multimodal Reasoning with Compositional Conformal Guarantees](https://mlsanity.com/paper/2603.00324/) (2026)
- [COFT: Counterfactual-Conformal Decoding for Fair Chain-of-Thought Reasoning in Large Language Models](https://mlsanity.com/paper/2605.30641/) (2026)
- [PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems](https://mlsanity.com/paper/2507.19067/) (2025)
- [Reproducing FACTER: Fairness via Conformal Thresholding and Prompt Repair](https://mlsanity.com/paper/2606.28620/) (2026)

## Abstract (arXiv)

We propose FACTER, a fairness-aware framework for LLM-based recommendation systems that integrates conformal prediction with dynamic prompt engineering. By introducing an adaptive semantic variance threshold and a violation-triggered mechanism, FACTER automatically tightens fairness constraints whenever biased patterns emerge. We further develop an adversarial prompt generator that leverages historical violations 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 recommendation accuracy, revealing semantic variance as a potent proxy of bias.