Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems

• URL: http://arxiv.org/abs/2602.02582v1
• Date: Sat, 31 Jan 2026 17:18:13 GMT
• Title: Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems
• Authors: Chandan Kumar Sah, Xiaoli Lian, Li Zhang, Tony Xu, Syed Shazaib Shah,
• Abstract summary: This paper studies how uncertainty and fairness evaluations affect the accuracy, consistency, and trustworthiness of large language models (LLMs)<n>We quantify predictive uncertainty (via entropy) and demonstrate that Google DeepMind's Gemini 1.5 Flash exhibits systematic unfairness for certain sensitive attributes.<n>We propose a novel uncertainty-aware evaluation methodology for RecLLMs, present empirical insights from deep uncertainty case studies, and introduce a personality profile-informed fairness benchmark.
• Score: 3.937681476010311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) enable powerful zero-shot recommendations by leveraging broad contextual knowledge, yet predictive uncertainty and embedded biases threaten reliability and fairness. This paper studies how uncertainty and fairness evaluations affect the accuracy, consistency, and trustworthiness of LLM-generated recommendations. We introduce a benchmark of curated metrics and a dataset annotated for eight demographic attributes (31 categorical values) across two domains: movies and music. Through in-depth case studies, we quantify predictive uncertainty (via entropy) and demonstrate that Google DeepMind's Gemini 1.5 Flash exhibits systematic unfairness for certain sensitive attributes; measured similarity-based gaps are SNSR at 0.1363 and SNSV at 0.0507. These disparities persist under prompt perturbations such as typographical errors and multilingual inputs. We further integrate personality-aware fairness into the RecLLM evaluation pipeline to reveal personality-linked bias patterns and expose trade-offs between personalization and group fairness. We propose a novel uncertainty-aware evaluation methodology for RecLLMs, present empirical insights from deep uncertainty case studies, and introduce a personality profile-informed fairness benchmark that advances explainability and equity in LLM recommendations. Together, these contributions establish a foundation for safer, more interpretable RecLLMs and motivate future work on multi-model benchmarks and adaptive calibration for trustworthy deployment.

Related papers

• Beyond Hallucinations: A Composite Score for Measuring Reliability in Open-Source Large Language Models [0.0]
  Large Language Models (LLMs) are increasingly used in decision-critical domains such as healthcare, law, and finance.<n>They often make overconfident errors, degrade under input shifts, and lack clear uncertainty estimates.<n>We introduce the Composite Reliability Score (CRS), a unified framework that integrates calibration, robustness, and uncertainty quantification into a single interpretable metric.
  arXiv  Detail & Related papers  (2025-12-30T08:07:28Z)
• Rubric-Conditioned LLM Grading: Alignment, Uncertainty, and Robustness [4.129847064263056]
  We systematically evaluate the performance of Large Language Models for rubric-based short-answer grading.<n>We find that alignment is strong for binary tasks but degrades with increased rubric granularity.<n>Experiments reveal that while the model is resilient to prompt injection, it is sensitive to synonym substitutions.
  arXiv  Detail & Related papers  (2025-12-21T05:22:04Z)
• TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them [58.04324690859212]
  Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks.<n>We identify two fundamental types of inconsistencies: Score-Comparison Inconsistency and Pairwise Transitivity Inconsistency.<n>We propose TrustJudge, a probabilistic framework that addresses these limitations through two key innovations.
  arXiv  Detail & Related papers  (2025-09-25T13:04:29Z)
• Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs [7.197702136906138]
  We propose an uncertainty-aware fairness metric, UCerF, to enable a fine-grained evaluation of model fairness.<n> observing data size, diversity, and clarity issues in current datasets, we introduce a new gender-occupation fairness evaluation dataset.<n>We establish a benchmark, using our metric and dataset, and apply it to evaluate the behavior of ten open-source AI systems.
  arXiv  Detail & Related papers  (2025-05-29T20:45:18Z)
• Uncertainty Profiles for LLMs: Uncertainty Source Decomposition and Adaptive Model-Metric Selection [4.151658495779136]
  Large language models (LLMs) often generate factually incorrect outputs, known as hallucinations.<n>We present a systematic framework for decomposing uncertainty into four distinct sources.<n>We propose a method for task specific metric/model selection guided by the alignment or divergence between their uncertainty characteristics and that of a given task.
  arXiv  Detail & Related papers  (2025-05-12T07:55:22Z)
• REVAL: A Comprehension Evaluation on Reliability and Values of Large Vision-Language Models [59.445672459851274]
  REVAL is a comprehensive benchmark designed to evaluate the textbfREliability and textbfVALue of Large Vision-Language Models.<n>REVAL encompasses over 144K image-text Visual Question Answering (VQA) samples, structured into two primary sections: Reliability and Values.<n>We evaluate 26 models, including mainstream open-source LVLMs and prominent closed-source models like GPT-4o and Gemini-1.5-Pro.
  arXiv  Detail & Related papers  (2025-03-20T07:54:35Z)
• Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation [8.635811152610604]
  Uncertainty Quantification (UQ) is crucial for ensuring the safety and robustness of AI systems.<n>We propose a label-confidence-aware (LCA) uncertainty estimation based on Kullback-Leibler divergence bridging between samples and label source.
  arXiv  Detail & Related papers  (2024-12-10T07:35:23Z)
• On the Fairness, Diversity and Reliability of Text-to-Image Generative Models [68.62012304574012]
  multimodal generative models have sparked critical discussions on their reliability, fairness and potential for misuse.<n>We propose an evaluation framework to assess model reliability by analyzing responses to global and local perturbations in the embedding space.<n>Our method lays the groundwork for detecting unreliable, bias-injected models and tracing the provenance of embedded biases.
  arXiv  Detail & Related papers  (2024-11-21T09:46:55Z)
• Identifying and Mitigating Social Bias Knowledge in Language Models [52.52955281662332]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases.<n>FAST surpasses state-of-the-art baselines with superior debiasing performance.<n>This highlights the potential of fine-grained debiasing strategies to achieve fairness in large language models.
  arXiv  Detail & Related papers  (2024-08-07T17:14:58Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling [69.83976050879318]
  In large language models (LLMs), identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions.
  arXiv  Detail & Related papers  (2023-11-15T05:58:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.