Enhancing Counterfactual Fairness in Domain Generalization for Evolving Environments

Belangrijkste concepten

The proposed CDSAE framework effectively separates environmental information and sensitive attributes from the embedded representation of classification features, thereby improving model generalization across diverse and unfamiliar domains while ensuring the preservation of fairness in evolving environments.

Samenvatting

The paper introduces a novel framework called Counterfactual Fairness-Aware Domain Generalization with Sequential Autoencoder (CDSAE) to address the challenge of domain generalization in the context of evolving environments while ensuring counterfactual fairness.
Key highlights:

The causal structure of CDSAE partitions the exogenous variables into four latent variables to model the relationships among sensitive attributes, environmental information, and semantic information.
By disentangling environmental information and sensitive attributes from the embedded representation of classification features, CDSAE enhances model generalization across diverse domains while preserving fairness.
The training process of CDSAE involves maximizing the Evidence Lower Bound (ELBO) and minimizing a fairness regularization term to achieve both improved accuracy and counterfactual fairness.
Experimental validation on synthetic and real-world datasets demonstrates the effectiveness of CDSAE in achieving superior predictive capabilities compared to existing methods while ensuring fairness preservation.
The authors introduce a novel synthetic dataset, Fair-circle, to address fairness issues in dynamically changing environments.

Statistieken

The closer the absolute values of Total Effect (TE) and Counterfactual Effect (CE) are to 0, the higher the adherence to counterfactual fairness.
CDSAE consistently achieves optimal or near-optimal TE and CE values across the tested datasets, indicating its resilience in maintaining high performance while upholding fairness principles.

Citaten

"By incorporating fairness regularization, we exclusively employ semantic information for classification purposes."
"Our strategy is rooted in the principles of causal inference to tackle these dual issues."
"Empirical validation on synthetic and real-world datasets substantiates the effectiveness of our approach, demonstrating improved accuracy levels while ensuring the preservation of fairness in the evolving landscape of continuous domains."

Belangrijkste Inzichten Gedestilleerd Uit

Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments

by Yujie Lin,Ch... om arxiv.org 05-07-2024

https://arxiv.org/pdf/2309.13005.pdf

Diepere vragen

How can the CDSAE framework be extended to handle more complex and diverse sensitive attributes beyond binary variables

To extend the CDSAE framework to handle more complex and diverse sensitive attributes beyond binary variables, several modifications and enhancements can be implemented. One approach is to incorporate multi-class sensitive attributes by expanding the framework to accommodate categorical variables with more than two levels. This would involve adjusting the encoding and disentanglement processes to capture the nuanced relationships between the sensitive attribute and other variables. Additionally, introducing a mechanism for continuous sensitive attributes can enhance the framework's flexibility to address a wider range of scenarios. By incorporating techniques such as ordinal encoding or embedding layers, the model can effectively handle continuous sensitive attributes. Furthermore, integrating techniques from multi-label classification can enable the framework to manage scenarios where multiple sensitive attributes are present simultaneously, allowing for a more comprehensive analysis of fairness considerations in complex datasets.

What are the potential limitations of the causal structure proposed in CDSAE, and how can it be further refined to address more intricate relationships between variables

While the causal structure proposed in CDSAE provides a solid foundation for addressing fairness and generalization challenges, there are potential limitations that could be further refined for improved performance. One limitation is the assumption of linear relationships between variables, which may not fully capture the complex interactions present in real-world datasets. To address this, incorporating non-linear causal relationships through the use of neural networks or kernel methods can enhance the model's ability to capture intricate dependencies between variables. Additionally, refining the disentanglement process to more accurately separate sensitive attributes from environmental information and semantic features can improve the model's interpretability and fairness preservation. By incorporating more sophisticated disentanglement techniques, such as adversarial training or mutual information maximization, the model can better isolate the causal factors influencing the outcomes. Moreover, exploring the integration of causal inference methods, such as do-calculus, can provide a more rigorous framework for analyzing causal relationships and ensuring fairness in decision-making processes.

Given the observed decline in CDSAE's performance in later testing domains, what additional techniques or modifications could be explored to maintain its robustness and adaptability over longer time horizons

To address the observed decline in CDSAE's performance in later testing domains and maintain its robustness over longer time horizons, several strategies can be explored. One approach is to implement continual learning techniques that allow the model to adapt and update its knowledge as new data from evolving domains becomes available. By incorporating mechanisms for online learning and domain adaptation, the model can continuously refine its representations and adapt to changing environments. Additionally, introducing regularization techniques to mitigate catastrophic forgetting and promote knowledge retention from previous domains can help sustain performance across sequential domains. Techniques such as elastic weight consolidation or replay mechanisms can be employed to stabilize the model's learning process and prevent performance degradation over time. Furthermore, exploring ensemble learning methods that combine multiple CDSAE models trained on different subsets of data can enhance the model's robustness and generalization capabilities, enabling it to maintain high performance across diverse and evolving domains.