cair-nepal/ai-bias-research-landscape huggingface dataset:
Title: CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System
Authors: Yashar Deldjoo; Tommaso Di Noia
Affiliations: Polytechnic University of Bari; Polytechnic University of Bari
Year: 2025
Domain: Recommender Systems

Abstract: This work takes a critical stance on previous studies concerning fairness evaluation in Large-Language Model (LLM)-based recommender systems, which have primarily assessed consumer fairness by comparing recommendation lists generated with and without sensitive user attributes. Such approaches implicitly treat discrepancies in recommended items as biases, overlooking whether these changes might stem from genuine personalization aligned with true preferences of users. Moreover, these earlier studies typically address single sensitive attributes in isolation, neglecting the complex interplay of intersectional identities. In response to these shortcomings, we introduce CFaiRLLM , an enhanced evaluation framework that not only incorporates true preference alignment but also rigorously examines intersectional fairness by considering overlapping sensitive attributes. Additionally, CFaiRLLM introduces diverse user profile sampling strategies— random , top-rated , and recency-focused —to better understand the impact of profile generation fed to LLMs in light of inherent token limitations in these systems. Given that fairness depends on accurately understanding users’ tastes and preferences, these strategies provide a more realistic assessment of fairness within RecLLMs. To validate the efficacy of CFaiRLLM, we conducted extensive experiments using MovieLens and LastFM datasets, applying various sampling strategies and sensitive attribute configurations. The evaluation metrics include both item similarity measures and true preference alignment considering both hit and ranking (Jaccard Similarity and PRAG), thereby conducting a multi-faceted analysis of recommendation fairness. The results demonstrated that true preference alignment offers a more personalized and fair assessment compared to similarity-based measures, revealing significant disparities when sensitive and intersectional attributes are incorporated. Notably, our study finds that intersectional attributes amplify fairness gaps more prominently, especially in less structured domains such as music recommendations in LastFM. These findings suggest that future fairness evaluations in RecLLMs should incorporate true preference alignment to ensure equitable and genuinely personalized recommendations.
Title: Dynamic Fairness-aware Recommendation Through Multi-agent Social Choice
Authors: Amanda Aird; Paresha Farastu; Joshua W. Sun; Elena Štefancová; Cassidy All; Amy Voida; Nicholas Mattei; Robin Burke
Affiliations: University of Colorado Boulder; University of Colorado Boulder; University of Colorado Boulder; Comenius University Bratislava; University of Colorado Boulder; University of Colorado Boulder; Tulane University; University of Colorado Boulder
Year: 2024
Domain: Recommender Systems

Abstract: Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks. Researchers studying classification have generally considered fairness to be a matter of achieving equality of outcomes (or some other metric) between a protected and unprotected group and built algorithmic interventions on this basis. We argue that fairness in real-world application settings in general, and especially in the context of personalized recommendation, is much more complex and multi-faceted, requiring a more general approach. To address the fundamental problem of fairness in the presence of multiple stakeholders, with different definitions of fairness, we propose the Social Choice for Recommendation Under Fairness–Dynamic architecture, which formalizes multistakeholder fairness in recommender systems as a two-stage social choice problem. In particular, we express recommendation fairness as a combination of an allocation and an aggregation problem, which integrate both fairness concerns and personalized recommendation provisions, and derive new recommendation techniques based on this formulation. We demonstrate the ability of our framework to dynamically incorporate multiple fairness concerns using both real-world and synthetic datasets.
Title: FairCRS: Towards User-oriented Fairness in Conversational Recommendation Systems
Authors: Q. L. Liu; Xuan Feng; Tianlong Gu; Xiaoli Liu
Affiliations: Jinan University; Jinan University; Jinan University; Jinan University
Year: 2024
Domain: Recommender Systems

Abstract: Conversational Recommendation Systems (CRSs) enable recommender systems to explicitly acquire user preferences during multi-turn interactions, providing more accurate and personalized recommendations. However, the data imbalance in CRSs, due to inconsistent interaction history among users, may lead to disparate treatment for disadvantaged user groups. In this paper, we investigate the discriminate problems in CRS from the user’s perspective, called as user-oriented fairness. To reveal the unfairness problems of different user groups in CRS, we conduct extensive empirical analyses. To mitigate user unfairness, we propose a user-oriented fairness framework, named FairCRS, which is a model-agnostic framework. In particular, we develop a user-embedding reconstruction mechanism that enriches user embeddings by incorporating more interaction information, and design a user-oriented fairness strategy that optimizes the recommendation quality differences among user groups while alleviating unfairness. Extensive experimental results on English and Chinese datasets show that FairCRS outperforms state-of-the-art CRSs in terms of overall recommendation performance and user fairness.
Title: Shift your Focus for the Greater Good: Improving Fairness at no cost for Accuracy and Diversity in News Recommender Systems
Authors: Célina Treuillier; Sylvain Castagnos; Evan Dufraisse; Özlem Özgöbek; Armelle Brun
Affiliations: University of Fribourg; Laboratoire Lorrain de Recherche en Informatique et ses Applications; Université Paris-Saclay; Norwegian University of Science and Technology; Laboratoire Lorrain de Recherche en Informatique et ses Applications
Year: 2026
Domain: Recommender Systems

Abstract: In today’s digital landscape, recommender systems assist users in navigating the vast amount of available data. Within the realm of information access, a subset of such systems called News Recommender Systems help users find news content that interests them. However, by prioritizing traditional accuracy-focused optimization, these systems contribute to the formation of filter bubbles, restricting users’ exposure to diverse viewpoints and exacerbating polarization. To address this issue, beyond-accuracy factors like diversity have been integrated into recommendation. Yet, such approaches can be ineffective and even unintentionally influence user opinions. This raises major ethical concerns as systems lack the legitimacy to shape opinions. This article presents the ADF framework, a novel approach designed to optimize accuracy, diversity, and fairness simultaneously. Unlike conventional models that manage fairness in a tradeoff, ADF establishes fairness as a core constraint. The framework relies on an innovative fairness-constrained diversification strategy, ensuring that users are exposed to a broader range of opinions, without being oriented toward specific viewpoints. ADF is adaptable to various diversity metrics and provides personalized diversification, independent of the underlying recommendation algorithms. Through real-world benchmark datasets and multiple recommendation models, experimental evaluation confirmed that ADF limits the impact on accuracy, enhances diversity, and crucially upholds fairness.
Title: Accuracy, Fairness, Diversity All at Once: An Influence-Function-Guided Data Enhancement Approach for Recommender System
Authors: Tianyang Xie; Yong Ge; Shuojia Guo
Affiliations: University of Arizona; University of Arizona; College of Staten Island
Year: 2025
Domain: Recommender Systems

Abstract: Recommender systems play a pivotal role in curating high-quality content for users, predominantly leveraging data-driven algorithms and machine learning methodologies. However, the intrinsic data-centric nature of these systems raises critical concerns; biased datasets and algorithms can inadvertently propagate biases to end-users. Furthermore, machine learning techniques, while powerful, can overfit a user’s preference, leading to a monotonous stream of content suggestions. Both the CS and IS community have well-recognized the need of fairness and diversity in recommender systems and many studies are proposed to mitigate these challenges. Yet, a tangible solution that holistically addressed all three components—accuracy, fairness, and diversity—in unison remains elusive. This article aims to bridge this gap, introducing a novel Influence-Function-Guided, Fair, and Diverse Data Enhancement (InFoDance) approach that enhances all three perspectives simultaneously. It consists of four interconnected modules: model training, candidate data generation, influence function-based candidate evaluation, and virtual data selection. It iteratively generates virtual data to update the trained recommender system. The empirical evaluation has shown that our approach can improve accuracy, fairness, and diversity by up to 24.27%, 55.29%, and 1.85% simultaneously and significantly outperform the state-of-the-art baselines on multiple evaluation metrics.
Title: Balancing Fairness and High Match Rates in Reciprocal Recommender Systems: A Nash Social Welfare Approach
Authors: Yoji Tomita; Tomohiro Yokoyama
Affiliations: CyberAgent (Japan); The University of Tokyo
Year: 2026
Domain: Recommender Systems

Abstract: Matching platforms, such as online dating services and job recommendations, have become increasingly prevalent. For the success of these platforms, it is crucial to design reciprocal recommender systems (RRSs) that not only increase the total number of matches but also avoid creating unfairness among users. In this paper, we investigate the fairness of RRSs on matching platforms. From the perspective of fair division, we define the users’ opportunities to be recommended and establish the fairness concept of envy-freeness in the allocation of these opportunities. We first introduce the Social Welfare (SW) method, which approximately maximizes the number of matches, and show that it leads to significant unfairness in recommendation opportunities, illustrating the trade-off between fairness and match rates. To address this challenge, we propose the Nash Social Welfare (NSW) method, which alternately optimizes two NSW functions and achieves nearly envy-free recommendations. We further generalize the SW and NSW method to the α -SW method, which balances the trade-off between fairness and high match rates. Additionally, we develop a computationally efficient approximation algorithm for the SW/NSW/ α -SW methods based on the Sinkhorn algorithm. Through extensive experiments on both synthetic datasets and two real-world datasets, we demonstrate the practical effectiveness of our approach.
Title: Relevance-aware Individual Item Fairness Measures for Recommender Systems: Limitations and Usage Guidelines
Authors: Theresia Veronika Rampisela; Maria Maistro; Tuukka Ruotsalo; Falk Scholer; Christina Lioma
Affiliations: University of Copenhagen; University of Copenhagen; University of Copenhagen; MIT University; University of Copenhagen
Year: 2025
Domain: Recommender Systems

Abstract: Recommender Systems (RSs) aim at providing relevant items to users, with a recent emphasis on improving recommendation fairness. Quantifying fairness of the recommended items can be done with two types of evaluation measures: measures that are purely based on item exposure ( exposure-based ) and measures that account for both item exposure and item relevance ( relevance-aware ). While exposure-based measures have been thoroughly analysed, relevance-aware measures have not been examined in such detail yet. We gather all existing relevance-aware individual item fairness measures for RSs and study their theoretical properties. We find that all measures suffer from one or more limitations, which may cause issues in their computation, interpretability, or expressiveness. To address this, we correct the affected measures or explain why a limitation is unresolvable. Further, we empirically investigate the extent of the limitations on the measures and compare the original measures to our reformulations under common and extreme evaluation scenarios across real-world and synthetic data. Our experiments show that our reformulated measures successfully resolve the issues in the original measures. We conclude by providing practical guidelines on how to select measures for a range of use cases.
Title: When Fairness meets Bias: a Debiased Framework for Fairness aware Top-N Recommendation
Authors: Jiakai Tang; Shiqi Shen; Zhipeng Wang; Zhi Gong; Jingsen Zhang; Xu Chen
Affiliations: Renmin University of China; Tencent (China); Tencent (China); Tencent (China); Renmin University of China; Renmin University of China
Year: 2023
Domain: Recommender Systems

Abstract: Fairness in the recommendation domain has recently attracted increasing attention due to more and more concerns about the algorithm discrimination and ethics. While recent years have witnessed many promising fairness aware recommender models, an important problem has been largely ignored, that is, the fairness can be biased due to the user personalized selection tendencies or the non-uniform item exposure probabilities. To study this problem, in this paper, we formally define a novel task named as unbiased fairness aware Top-N recommendation. For solving this task, we firstly define an ideal loss function based on all the user-item pairs. Considering that, in real-world datasets, only a small number of user-item interactions can be observed, we then approximate the above ideal loss with a more tractable objective based on the inverse propensity score (IPS). Since the recommendation datasets can be noisy and quite sparse, which brings difficulties for accurately estimating the IPS, we propose to optimize the objective in an IPS range instead of a specific point, which improves the model fault tolerance capability. In order to make our model more applicable to the commonly studied Top-N recommendation, we soften the ranking metrics such as Precision, Hit-Ratio, and NDCG to derive a fully differentiable framework. We conduct extensive experiments to demonstrate the effectiveness of our model based on four real-world datasets.
Title: On Inherited Popularity Bias in Cold-Start Item Recommendation
Authors: G. R. Meehan; Johan Pauwels
Affiliations: Queen Mary University of London; Queen Mary University of London
Year: 2025
Domain: Recommender Systems

Abstract: Collaborative filtering (CF) recommender systems struggle with making predictions on unseen, or 'cold', items. Systems designed to address this challenge are often trained with supervision from warm CF models in order to leverage collaborative and content information from the available interaction data. However, since they learn to replicate the behavior of CF methods, cold-start models may therefore also learn to imitate their predictive biases. In this paper, we show that cold-start systems can inherit popularity bias, a common cause of recommender system unfairness arising when CF models overfit to more popular items, thereby maximizing user-oriented accuracy but neglecting rarer items. We demonstrate that cold-start recommenders not only mirror the popularity biases of warm models, but are in fact affected more severely: because they cannot infer popularity from interaction data, they instead attempt to estimate it based solely on content features. This leads to significant over-prediction of certain cold items with similar content to popular warm items, even if their ground truth popularity is very low. Through experiments on three multimedia datasets, we analyze the impact of this behavior on three generative cold-start methods. We then describe a simple post-processing bias mitigation method that, by using embedding magnitude as a proxy for predicted popularity, can produce more balanced recommendations with limited harm to user-oriented cold-start accuracy.
Title: Opening the Black Box: Interpretable Remedies for Popularity Bias in Recommender Systems
Authors: Parviz Ahmadov; Masoud Mansoury
Affiliations: Delft University of Technology; Delft University of Technology
Year: 2025
Domain: Recommender Systems

Abstract: Popularity bias is a well-known challenge in recommender systems, where a small number of popular items receive disproportionate attention, while the majority of less popular items are largely overlooked.This imbalance often results in reduced recommendation quality and unfair exposure of items.Although existing mitigation techniques address this bias to some extent, they typically lack transparency in how they operate.In this paper, we propose a post-hoc method using a Sparse Autoencoder (SAE) to interpret and mitigate popularity bias in deep recommendation models.The SAE is trained to replicate a pre-trained model's behavior while enabling neuron-level interpretability.By introducing synthetic users with clear preferences for either popular or unpopular items, we identify neurons encoding popularity signals based on their activation patterns.We then adjust the activations of the most biased neurons to steer recommendations toward fairer exposure.Experiments on two public datasets using a sequential recommendation model show that our method significantly improves fairness with minimal impact on accuracy.Moreover, it offers interpretability and fine-grained control over the fairness-accuracy trade-off.
Title: Rewriting Bias: Automated Rewriting to Reduce Media Bias Polarisation in News Recommender Systems
Authors: Qin Ruan; Brian Mac Namee; Aonghus Lawlor; Susan Leavy; Ruihai Dong
Affiliations: University College Dublin; University College Dublin; University College Dublin; University College Dublin; University College Dublin
Year: 2025
Domain: Recommender Systems

Abstract: Media bias in news reporting significantly influences public perception and societal dynamics. Personalised news recommender systems, widely adopted by digital platforms, often exacerbate patterns of polarisation by amplifying users’ pre-existing preferences, creating filter bubbles, and reinforcing ideological divides. To address this issue, this work explores sentence rewriting as a novel strategy to mitigate polarisation in the dissemination of biased news content. We propose automated techniques leveraging word replacement methods and large language models (LLMs) to rewrite biased news content into neutral versions while preserving meaning and readability. Extensive experiments evaluate the effectiveness of sentence rewriting across various components of the news recommendation feedback loop, including user reading histories, candidate news datasets, and training datasets. Experimental results demonstrate the ability of sentence rewriting to reduce media bias polarisation in the dissemination of biased news content while maintaining or enhancing recommendation accuracy. Furthermore, this work provides a comprehensive assessment of media bias polarisation mitigation through a multidimensional evaluation framework and examines the ethical and societal challenges of addressing media bias polarisation in news recommendations. It also highlights the need for collaborative solutions involving platforms, news organisations, and users to foster more transparent news recommender systems.
Title: Investigating User-Side Fairness in Outcome and Process for Multi-Type Sensitive Attributes in Recommendations
Authors: Weixin Chen; Li Chen; Yuhan Zhao
Affiliations: Hong Kong Baptist University; Hong Kong Baptist University; Hong Kong Baptist University
Year: 2025
Domain: Recommender Systems

Abstract: Recommender systems have been popularly employed to address information overload problems for users, for which unfairness issues are essential to be mitigated. Current user-side fairness studies in recommendations aim to ensure the independence of users’ sensitive attributes in terms of outcome or process . Specifically, the former emphasizes the outcome metric equity across user groups based on sensitive attributes, for which regularization has been proposed as a typical approach to reducing the recommendation imbalance among them. The latter process fairness focuses on independence between sensitive attributes and recommendations in the process, for which adversarial learning has been popularly adopted to remove sensitive information from user representation for individual users. However, little work has investigated the effects of these methods on balancing both group-level outcome fairness and individual-level process fairness in a particular scenario. Moreover, existing experiments have primarily been performed on one type of user attributes, e.g., behavioral attributes or demographic attributes, but neglected other possible sensitive attributes such as psychological attributes. In this article, we have investigated both outcome and process fairness performance of regularization and adversarial learning methods over multiple types of sensitive attributes, including behavioral (e.g., activity level), demographic (e.g., gender, age), and psychological attributes (e.g., big-five personality, curiosity). The experiments on four datasets show that unfairness can exist in different forms for different types of sensitive attributes, and relative to the regularization-based method, adversarial learning owns a higher potential to achieve a balance between outcome and process user-side fairness in recommendations. Our source code is available at https://github.com/WeixinChen98/OtPrFairness-MultiAttr .
Title: Long-term Dynamics of Fairness Intervention in Connection Recommender Systems
Authors: Nil-Jana Akpinar; Cyrus DiCiccio; Preetam Nandy; Kinjal Basu
Affiliations: Carnegie Mellon University; LinkedIn (United States); LinkedIn (United States); LinkedIn (United States)
Year: 2022
Domain: Recommender Systems

Abstract: Recommender system fairness has been studied from the perspectives of a variety of stakeholders including content producers, the content itself and recipients of recommendations. Regardless of which type of stakeholders are considered, most works in this area assess the efficacy of fairness intervention by evaluating a single fixed fairness criterion through the lens of a one-shot, static setting. Yet recommender systems constitute dynamical systems with feedback loops from the recommendations to the underlying population distributions which could lead to unforeseen and adverse consequences if not taken into account. In this paper, we study a connection recommender system patterned after the systems employed by web-scale social networks and analyze the long-term effects of intervening on fairness in the recommendations. We find that, although seemingly fair in aggregate, common exposure and utility parity interventions fail to mitigate amplification of biases in the long term. We theoretically characterize how certain fairness interventions impact the bias amplification dynamics in a stylized Polya urn model.