A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System

• URL: http://arxiv.org/abs/2405.02219v2
• Date: Wed, 11 Sep 2024 07:27:51 GMT
• Title: A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System
• Authors: Yashar Deldjoo, Fatemeh Nazary,
• Abstract summary: This paper proposes a normative framework to benchmark consumer fairness in LLM-powered recommender systems. We argue that this gap can lead to arbitrary conclusions about fairness. Experiments on the MovieLens dataset on consumer fairness reveal fairness deviations in age-based recommendations.
• Score: 9.470545149911072
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid adoption of large language models (LLMs) in recommender systems (RS) presents new challenges in understanding and evaluating their biases, which can result in unfairness or the amplification of stereotypes. Traditional fairness evaluations in RS primarily focus on collaborative filtering (CF) settings, which may not fully capture the complexities of LLMs, as these models often inherit biases from large, unregulated data. This paper proposes a normative framework to benchmark consumer fairness in LLM-powered recommender systems (RecLLMs). We critically examine how fairness norms in classical RS fall short in addressing the challenges posed by LLMs. We argue that this gap can lead to arbitrary conclusions about fairness, and we propose a more structured, formal approach to evaluate fairness in such systems. Our experiments on the MovieLens dataset on consumer fairness, using in-context learning (zero-shot vs. few-shot) reveal fairness deviations in age-based recommendations, particularly when additional contextual examples are introduced (ICL-2). Statistical significance tests confirm that these deviations are not random, highlighting the need for robust evaluation methods. While this work offers a preliminary discussion on a proposed normative framework, our hope is that it could provide a formal, principled approach for auditing and mitigating bias in RecLLMs. The code and dataset used for this work will be shared at "gihub-anonymized".

Related papers

• Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge [84.34545223897578]
  Despite their excellence in many domains, potential issues are under-explored, undermining their reliability and the scope of their utility. We identify 12 key potential biases and propose a new automated bias quantification framework-CALM- which quantifies and analyzes each type of bias in LLM-as-a-Judge. Our work highlights the need for stakeholders to address these issues and remind users to exercise caution in LLM-as-a-Judge applications.
  arXiv  Detail & Related papers  (2024-10-03T17:53:30Z)
• Challenging Fairness: A Comprehensive Exploration of Bias in LLM-Based Recommendations [3.5297361401370044]
  Large Language Model (LLM)-based recommendation systems provide more comprehensive recommendations than traditional systems. These systems often exhibit biases, favoring mainstream content while marginalizing non-traditional options due to skewed training data. This study investigates the intricate relationship between bias and LLM-based recommendation systems.
  arXiv  Detail & Related papers  (2024-09-17T01:37:57Z)
• Editable Fairness: Fine-Grained Bias Mitigation in Language Models [52.66450426729818]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases. FAST surpasses state-of-the-art baselines with superior debiasing performance. 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)
• An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use Cases [0.0]
  This paper aims to provide a technical guide for practitioners to assess bias and fairness risks in large language models. The main contribution of this work is a decision framework that allows practitioners to determine which metrics to use for a specific LLM use case.
  arXiv  Detail & Related papers  (2024-07-15T16:04:44Z)
• Inducing Group Fairness in LLM-Based Decisions [12.368678951470162]
  Group fairness in Prompting Large Language Models (LLMs) is a well-studied problem. We show that prompt-based classifiers may lead to unfair decisions. We introduce several remediation techniques and benchmark their fairness and performance trade-offs.
  arXiv  Detail & Related papers  (2024-06-24T15:45:20Z)
• 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)
• Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency [9.882829614199453]
  This paper explores the biases in ChatGPT-based recommender systems, focusing on provider fairness (item-side fairness) In the first experiment, we assess seven distinct prompt scenarios on top-K recommendation accuracy and fairness. Embedding fairness into system roles, such as "act as a fair recommender," proved more effective than fairness directives within prompts.
  arXiv  Detail & Related papers  (2024-01-19T08:09:20Z)
• Marginal Debiased Network for Fair Visual Recognition [59.05212866862219]
  We propose a novel marginal debiased network (MDN) to learn debiased representations. Our MDN can achieve a remarkable performance on under-represented samples.
  arXiv  Detail & Related papers  (2024-01-04T08:57:09Z)
• GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models [83.30078426829627]
  Large language models (LLMs) have gained popularity and are being widely adopted by a large user community. The existing evaluation methods have many constraints, and their results exhibit a limited degree of interpretability. We propose a bias evaluation framework named GPTBIAS that leverages the high performance of LLMs to assess bias in models.
  arXiv  Detail & Related papers  (2023-12-11T12:02:14Z)
• Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation [52.62492168507781]
  We propose a novel benchmark called Fairness of Recommendation via LLM (FaiRLLM) This benchmark comprises carefully crafted metrics and a dataset that accounts for eight sensitive attributes. By utilizing our FaiRLLM benchmark, we conducted an evaluation of ChatGPT and discovered that it still exhibits unfairness to some sensitive attributes when generating recommendations.
  arXiv  Detail & Related papers  (2023-05-12T16:54:36Z)

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.