Compositional Fairness Constraints for Graph Embeddings (1905.10674v4)

Abstract: Learning high-quality node embeddings is a key building block for machine learning models that operate on graph data, such as social networks and recommender systems. However, existing graph embedding techniques are unable to cope with fairness constraints, e.g., ensuring that the learned representations do not correlate with certain attributes, such as age or gender. Here, we introduce an adversarial framework to enforce fairness constraints on graph embeddings. Our approach is compositional---meaning that it can flexibly accommodate different combinations of fairness constraints during inference. For instance, in the context of social recommendations, our framework would allow one user to request that their recommendations are invariant to both their age and gender, while also allowing another user to request invariance to just their age. Experiments on standard knowledge graph and recommender system benchmarks highlight the utility of our proposed framework.

Compositional Fairness Constraints for Graph Embeddings

The paper, "Compositional Fairness Constraints for Graph Embeddings," addresses the challenge of enforcing fairness constraints in graph embeddings. Traditional graph embedding techniques transform graph-structured data into low-dimensional vectors, vital for tasks like link prediction in social networks and recommender systems. However, these methods often lack mechanisms to ensure fairness, potentially leading to embeddings that inadvertently encode sensitive attributes such as age or gender.

The authors propose an adversarial framework that introduces compositional fairness constraints, allowing the generated embeddings to be invariant to a subset of sensitive attributes as per user requirements. This approach is particularly relevant in contexts like social media, where users might demand personalized fairness constraints, such as independence from both age and gender, or only from age, depending on their preference.

Methodological Contributions

At the core of this research is the adaptation of an adversarial learning framework, inspired by related works in algorithmic fairness and adversarial training. This framework equips graph embeddings with the ability to filter out specific sensitive information. The model's design includes a set of filters and discriminators: filters are trained to eliminate information related to sensitive attributes, while discriminators attempt to identify such attributes from the embeddings. The model's ability to apply these filters compositionally allows it to handle a combinatorially large space of sensitive attribute subsets.

The paper also addresses the distinctiveness of graph-structured data, which presents challenges due to their non-i.i.d. nature. The compositional nature of the proposed methodology allows for flexibility and scalability in enforcing fairness across diverse applications.

Empirical Validation

The framework's efficacy is demonstrated on several datasets, including Freebase 15k-237, MovieLens-1M, and Reddit, each containing different sensitive attributes. Extensive experiments reveal several critical insights:

1. Trade-off Analysis: The research quantifies the trade-off between removing sensitive attribute information and maintaining accuracy on primary prediction tasks. Results indicate that while fairness constraints can lead to a performance trade-off, the degradation is manageable (approximately a 10% increase in error on certain tasks), making the approach viable for real-world applications.
2. Compositionality: Notably, the compositional framework outperformed non-compositional approaches in removing sensitive information, particularly on datasets where sensitive attributes are correlated. This highlights the advantage of compositional filters in leveraging attribute interdependencies for enhanced fairness.
3. Generalization to Unseen Attribute Combinations: A defining feature of the proposed method is its ability to handle combinations of attributes not explicitly seen during training. This capability is empirically validated in experiments on the Reddit dataset, demonstrating the model's effectiveness even on unseen sensitive attribute sets.

Implications and Future Directions

The research carries significant implications for the design of fair machine learning models, particularly in domains characterized by complex relational data. By providing a mechanism to enforce fairness without substantial performance penalties, this framework offers a practical solution for ensuring unbiased predictions in systems reliant on graph embeddings, such as social recommendation engines.

Looking forward, the paper opens avenues for further exploration, such as investigating other adversarial strategies and non-adversarial regularizers to enhance fairness. Additionally, the dynamics of user-driven fairness constraints in real-world applications, where users' preferences might introduce new biases, warrant further exploration to optimize the deployment of such systems.

In summary, this work advances the field by bridging the gap between fairness and ethical implications in graph embedding models, offering a robust framework that balances fairness and accuracy in social and multi-relational datasets.