Adversarial Learning for Debiasing Knowledge Graph Embeddings
Knowledge Graphs (KG) are gaining increasing attention in both academia and industry. Despite their diverse benefits, recent research have identified social and cultural biases embedded in the representations learned from KGs. Such biases can have detrimental consequences on different population and minority groups as applications of KG begin to intersect and interact with social spheres. This paper aims at identifying and mitigating such biases in Knowledge Graph (KG) embeddings. As a first step, we explore popularity bias -- the relationship between node popularity and link prediction accuracy. In case of node2vec graph embeddings, we find that prediction accuracy of the embedding is negatively correlated with the degree of the node. However, in case of knowledge-graph embeddings (KGE), we observe an opposite trend. As a second step, we explore gender bias in KGE, and a careful examination of popular KGE algorithms suggest that sensitive attribute like the gender of a person can be predicted from the embedding. This implies that such biases in popular KGs is captured by the structural properties of the embedding. As a preliminary solution to debiasing KGs, we introduce a novel framework to filter out the sensitive attribute information from the KG embeddings, which we call FAN (Filtering Adversarial Network). We also suggest the applicability of FAN for debiasing other network embeddings which could be explored in future work.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeKnowledge Graph EmbeddingsKnowledge GraphsLink PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation Embeddings
Self-supervised learning has revolutionized medical imaging by enabling efficient and generalizable feature extraction from large-scale unlabeled datasets. Recently, self-supervised foundation models have been extended t…
Computed Tomography (CT)FairnessSelf-Supervised LearningDebiasing knowledge graph embeddings
It has been shown that knowledge graph embeddings encode potentially harmful social biases, such as the information that women are more likely to be nurses, and men more likely to be bankers. As graph embeddings begin to…
Knowledge Graph EmbeddingsCausally Disentangled Contrastive Learning for Multilingual Speaker Embeddings
Self-supervised speaker embeddings are widely used in speaker verification systems, but prior work has shown that they often encode sensitive demographic attributes, raising fairness and privacy concerns. This paper inve…
Contrastive LearningSpeaker VerificationUsing Adversarial Debiasing to Remove Bias from Word Embeddings
Word Embeddings have been shown to contain the societal biases present in the original corpora. Existing methods to deal with this problem have been shown to only remove superficial biases. The method of Adversarial Debi…
Word EmbeddingsTowards Automatic Bias Detection in Knowledge Graphs
With the recent surge in social applications relying on knowledge graphs, the need for techniques to ensure fairness in KG based methods is becoming increasingly evident. Previous works have demonstrated that KGs are pro…
Bias DetectionFairnessKnowledge Graph EmbeddingsKnowledge Graphs