Fairness-Aware Explainable Recommendation over Knowledge Graphs

Fu, Zuohui; Xian, Yikun; Gao, Ruoyuan; Zhao, Jieyu; Huang, Qi‐Fang; Ge, Yingqiang; Xu, Shuyuan; Geng, Shijie; Shah, Chirag; Zhang, Yongfeng; Melo, Gerard de

doi:10.1145/3397271.3401051

Cited by 127 publications

(68 citation statements)

References 36 publications

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“…Ge et al [16] explore long-term fairness in recommendation and accomplish the problem through dynamic fairness learning. Fu et al [15] propose a fairness constrained approach to mitigate the unfairness problem in the context of explainable recommendation over knowledge graphs. They find that performance bias exists between different user groups, and claim that such bias comes from the different distribution of path diversity.…”

Section: Fair Recommendationmentioning

confidence: 99%

User-oriented Fairness in Recommendation

Chen

et al. 2021

Proceedings of the Web Conference 2021

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124

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As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects the system performance. Therefore, it is important to identify and solve the unfairness issues in recommendation scenarios.In this paper, we address the unfairness problem in recommender systems from the user perspective. We group users into advantaged and disadvantaged groups according to their level of activity, and conduct experiments to show that current recommender systems will behave unfairly between two groups of users. Specifically, the advantaged users (active) who only account for a small proportion in data enjoy much higher recommendation quality than those disadvantaged users (inactive). Such bias can also affect the overall performance since the disadvantaged users are the majority. To solve this problem, we provide a re-ranking approach to mitigate this unfairness problem by adding constraints over evaluation metrics. The experiments we conducted on several real-world datasets with various recommendation algorithms show that our approach can not only improve group fairness of users in recommender systems, but also achieve better overall recommendation performance. CCS CONCEPTS• Information systems → Recommender systems; • Computing methodologies → Artificial intelligence.

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Section: Fair Recommendationmentioning

confidence: 99%

User-oriented Fairness in Recommendation

Chen

et al. 2021

Proceedings of the Web Conference 2021

Self Cite

124

120

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“…Interpretability: The most common view of interpretability in RS is to increase the transparency of algorithms [14], [15], [40], [43], [164], which is especially important in health RS. Reliable explanations can greatly improve end-users' confidence in the recommendation results [126].…”

Section: Challengesmentioning

confidence: 99%

An Overview of Recommendation Techniques and Their Applications in Healthcare

Yue

Wang

Zhang

et al. 2021

IEEE/CAA J. Autom. Sinica

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With the increasing amount of information on the internet, recommendation system (RS) has been utilized in a variety of fields as an efficient tool to overcome information overload. In recent years, the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health. This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare. More concretely, an overview is provided on three famous recommendation techniques, namely, content-based, CF-based, and hybrid methods. Next, we provide a snapshot of five application scenarios about health RS, which are dietary recommendation, lifestyle recommendation, training recommendation, decision-making for patients and physicians, and disease-related prediction. Finally, some key challenges are given with clear justifications to this new and booming field.

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“…The FairFace dataset [65] is a collection of ≈100 thousand facial images extracted from the YFCC-100M Flickr dataset [165]. Automated models trained on FairFace can exploit age group (age ranges of [0-2], [3][4][5][6][7][8][9], [10][11][12][13][14][15][16][17][18][19], [20][21][22][23][24][25][26][27][28][29], [30][31][32][33][34][35][36][37][38][39], [40][41][42][43][44][45][46][47][48][49], [50]…”

Section: The Datasetmentioning

confidence: 99%

“…Unfortunately few works in the literature have tried to address more than one of these problems simultaneously. Some have tried to face two of them: for example some works combine fairness with privacy [33][34][35][36][37][38][39][40][41], others [42][43][44] combine adversarial learning with fairness, fairness with explainability [45][46][47], adversarial learning with explainability [48,49], and adversarial learning with privacy [50][51][52][53]. For this reason, in this work, we drive toward the development of systems able to ensure trustworthiness by delivering privacy, fairness, and explainability by design.…”

Section: Introductionmentioning

confidence: 99%

Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition

Franco

Oneto

Navarin

et al. 2021

Entropy

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In many decision-making scenarios, ranging from recreational activities to healthcare and policing, the use of artificial intelligence coupled with the ability to learn from historical data is becoming ubiquitous. This widespread adoption of automated systems is accompanied by the increasing concerns regarding their ethical implications. Fundamental rights, such as the ones that require the preservation of privacy, do not discriminate based on sensible attributes (e.g., gender, ethnicity, political/sexual orientation), or require one to provide an explanation for a decision, are daily undermined by the use of increasingly complex and less understandable yet more accurate learning algorithms. For this purpose, in this work, we work toward the development of systems able to ensure trustworthiness by delivering privacy, fairness, and explainability by design. In particular, we show that it is possible to simultaneously learn from data while preserving the privacy of the individuals thanks to the use of Homomorphic Encryption, ensuring fairness by learning a fair representation from the data, and ensuring explainable decisions with local and global explanations without compromising the accuracy of the final models. We test our approach on a widespread but still controversial application, namely face recognition, using the recent FairFace dataset to prove the validity of our approach.

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Fairness-Aware Explainable Recommendation over Knowledge Graphs

Cited by 127 publications

References 36 publications

User-oriented Fairness in Recommendation

User-oriented Fairness in Recommendation

An Overview of Recommendation Techniques and Their Applications in Healthcare

Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition

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