Towards Personalized Fairness based on Causal Notion

Li, Yunqi; Chen, Hanxiong; Xu, Shuyuan; Ge, Yingqiang; Zhang, Yongfeng

doi:10.1145/3404835.3462966

Cited by 77 publications

(62 citation statements)

References 51 publications

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“…There have been growing concerns on fairness in recommendation as recommender systems touch and influence more and more people in their daily lives. Several recent works have found various types of bias in recommendations, such as gender and race [2,8], item popularity [15,16,59], user feedback [13,25,27] and opinion polarity [54]. There are two primary paradigms adopted in recent studies on algorithmic discrimination: individual fairness and group fairness.…”

Section: Related Work 21 Fairness In Recommendationmentioning

confidence: 99%

“…For example, the "Matthew Effect" becomes increasingly evident in RS, which creates a huge disparity in the exposure of the producers/products in real-world recommendation systems [16,18,33]. Fortunately, these concerns about algorithmic fairness have resulted in a resurgence of interest to develop fairness-aware recommendation models to ensure such models do not become a source of unfair discrimination in recommendation [13,15,26,28].…”

Section: Introductionmentioning

confidence: 99%

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Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning

Zhao

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems (RS) touch and influence more and more people in their daily lives. In fairness-aware recommendation, most of the existing algorithmic approaches mainly aim at solving a constrained optimization problem by imposing a constraint on the level of fairness while optimizing the main recommendation objective, e.g., click through rate (CTR). While this alleviates the impact of unfair recommendations, the expected return of an approach may significantly compromise the recommendation accuracy due to the inherent trade-off between fairness and utility. This motivates us to deal with these conflicting objectives and explore the optimal trade-off between them in recommendation. One conspicuous approach is to seek a Pareto efficient/optimal solution to guarantee optimal compromises between utility and fairness. Moreover, considering the needs of real-world e-commerce platforms, it would be more desirable if we can generalize the whole Pareto Frontier, so that the decision-makers can specify any preference of one objective over another based on their current business needs. Therefore, in this work, we propose a fairness-aware recommendation framework using multi-objective reinforcement learning (MORL), called MoFIR (pronounced "more fair"), which is able to learn a single parametric representation for optimal recommendation policies over the space of all possible preferences. Specially, we modify traditional Deep Deterministic Policy Gradient (DDPG) by introducing conditioned network (CN) into it, which conditions the networks directly on these preferences and outputs Q-value-vectors. Experiments on several real-world recommendation datasets verify the superiority of our framework on both fairness metrics and recommendation measures when compared with all other baselines. We also extract the approximate Pareto Frontier on real-world datasets generated by MoFIR and compare to state-of-the-art fairness methods.

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Section: Related Work 21 Fairness In Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning

Zhao

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Self Cite

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“…The issue of fairness in recommendation has received growing concerns as recommender systems touch and influence people's daily lives more deeply and profoundly [28,41,62]. Several recent works focusing on fairness quantification have found various types of bias and unfairness in recommendations, such as gender and race [12,41,69], item popularity [1,2,24,28], and user activeness [18,40].…”

Section: Fairness In Recommendationmentioning

confidence: 99%

Explainable Fairness in Recommendation

Ge,

Tan,

Zhu

et al. 2022

Preprint

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Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies a more substantial problem-identifying the underlying reason of model disparity in recommendation. This information is critical for recommender system designers to understand the intrinsic recommendation mechanism and provides insights on how to improve model fairness to decision makers. Fortunately, with the rapid development of Explainable AI, we can use model explainability to gain insights into model (un)fairness. In this paper, we study the problem of explainable fairness, which helps to gain insights about why a system is fair or unfair, and guides the design of fair recommender systems with a more informed and unified methodology. Particularly, we focus on a common setting with feature-aware recommendation and exposure unfairness, but the proposed explainable fairness framework is general and can be applied to other recommendation settings and fairness definitions. We propose a Counterfactual Explainable Fairness framework, called CEF, which generates explanations about model fairness that can improve the fairness without significantly hurting the performance. The CEF framework formulates an optimization problem to learn the "minimal" change of the input features that changes the recommendation results to a certain level of fairness. Based on the counterfactual recommendation result of each feature, we calculate an explainability score in terms of the fairness-utility trade-off to rank all the feature-based explanations, and select the top ones as fairness explanations. Experimental results on several real-world datasets validate that our method is able to effectively provide explanations to the model disparities and these explanations can achieve better fairness-utility trade-off when using them for recommendation than all the baselines. CCS CONCEPTS• Computing methodologies → Artificial intelligence.

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“…In Table 1, for each reproducible paper, we identified the recommendation task (RP : Rating Prediction; TR : Top-N Recommendation), the notion of consumer fairness (EQ : equity of the error/utility score across demographic groups; IND : independence of the predicted relevance scores or recommendations from the demographic group), the consumers' grouping (G : Gender, A : Age, O : Occupation, B : Behavioral), the mitigation type (PRE-, IN-or POST-Processing), the evaluation data sets (ML : MovieLens 1M or 10M, LFM : LastFM 1K or 360K, AM: Amazon, SS: Sushi, SY: Synthetic), the utility/accuracy metrics (NDCG : Normalized Discounted Cumulative Gain; F1 : F1 Score; AUC: Area Under Curve; MRR : Mean Reciprocal Rank; RMSE : Root Mean-Square Error; MAE : Mean Ab- We identified [26,27,20,25] and [4,17,14] as non-reproducible procedures according to our criteria for top-n recommendation and rating prediction, respectively.…”

Section: Mitigation Procedures Collectionmentioning

confidence: 99%

Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

Boratto¹,

Fenu²,

Marras³

et al. 2022

Preprint

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Enabling non-discrimination for end-users of recommender systems by introducing consumer fairness is a key problem, widely studied in both academia and industry. Current research has led to a variety of notions, metrics, and unfairness mitigation procedures. The evaluation of each procedure has been heterogeneous and limited to a mere comparison with models not accounting for fairness. It is hence hard to contextualize the impact of each mitigation procedure w.r.t. the others. In this paper, we conduct a systematic analysis of mitigation procedures against consumer unfairness in rating prediction and top-n recommendation tasks. To this end, we collected 15 procedures proposed in recent top-tier conferences and journals. Only 8 of them could be reproduced. Under a common evaluation protocol, based on two public data sets, we then studied the extent to which recommendation utility and consumer fairness are impacted by these procedures, the interplay between two primary fairness notions based on equity and independence, and the demographic groups harmed by the disparate impact. Our study finally highlights open challenges and future directions in this field.

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Towards Personalized Fairness based on Causal Notion

Cited by 77 publications

References 51 publications

Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning

Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning

Explainable Fairness in Recommendation

Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

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