Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in...
Meta-learningDeep learningArtificial intelligenceFairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. Previous fairness-aware recommender systems assume that ...
Fairness-aware classifier with prejudice remover regularizer. In Joint Eur. Conf. Machine Learning and Knowledge Discovery in Databases 35–50 (Springer, 2012). Zafar, M. B., Valera, I., Rogriguez, M. G. & Gummadi, K. P. Fairness constraints: mechanisms for fair classification. In ...
If you find our work useful for your research, please cite our work: 📋 Contents 🌟 Introduction In this survey, we provide a comprehensive review of emerging and pressing issues related to bias and unfairness in three key stages of the integration of LLMs into IR systems. ...
Learning disentangled representation for fair facial attribute classification via fairness-aware information alignment Proc. AAAI Conf. Artif. Intell., 35 (2021), pp. 2403-2411 CrossrefView in ScopusGoogle Scholar [67] A. Gronowski, W. Paul, F. Alajaji, B. Gharesifard, P. Burlina Rényi ...
If the public is aware of these and has sufficient behavioral control to use them, this should result in perceptions of feeling less worse off due to carbon pricing. Research has indeed shown that perceiving emissions reduction options heightens carbon price acceptance (Merten et al., 2022). ...
we propose FairCF framework for fairness-aware collaborative filtering. In particular, we first define fairness constraints in a fair embedding space, where both a user classifier and an item classifier are employed to fit the fairness constraints. We then design an item classifier without item sens...
PDFM: A Primal-Dual Fairness-Aware Framework for Meta-learningChen ZhaoFeng ChenZhuoyi WangLatifur Khan
In recent years, fairness has become an important topic in the machine learning research community. In particular, counterfactual fairness aims at building prediction models which ensure fairness at the most individual level. Rather than globally considering equity over the entire population, the idea ...
In contrast to offline working fashions, two research paradigms are devised for online learning: (1) Online Meta Learning (OML) learns good priors over model parameters (or learning to learn) in a sequential setting where tasks are revealed one after another. Although it provides a sub-linear...