Enforcing Consistency and Fairness in Multi-level Hierarchical Classification with a Mask-based Output Layer
This addresses predictive reliability issues in sectors like e-commerce, healthcare, and education, though it is incremental as it builds on existing hierarchical classification methods.
The paper tackled the problem of inconsistent and unfair predictions in multi-level hierarchical classification by introducing a fair, model-agnostic layer that enforces taxonomy and optimizes objectives like consistency and fairness, resulting in improved fairness and accuracy compared to existing methods.
Traditional Multi-level Hierarchical Classification (MLHC) classifiers often rely on backbone models with $n$ independent output layers. This structure tends to overlook the hierarchical relationships between classes, leading to inconsistent predictions that violate the underlying taxonomy. Additionally, once a backbone architecture for an MLHC classifier is selected, adapting the model to accommodate new tasks can be challenging. For example, incorporating fairness to protect sensitive attributes within a hierarchical classifier necessitates complex adjustments to maintain the class hierarchy while enforcing fairness constraints. In this paper, we extend this concept to hierarchical classification by introducing a fair, model-agnostic layer designed to enforce taxonomy and optimize specific objectives, including consistency, fairness, and exact match. Our evaluations demonstrate that the proposed layer not only improves the fairness of predictions but also enforces the taxonomy, resulting in consistent predictions and superior performance. Compared to Large Language Models (LLMs) employing in-processing de-biasing techniques and models without any bias correction, our approach achieves better outcomes in both fairness and accuracy, making it particularly valuable in sectors like e-commerce, healthcare, and education, where predictive reliability is crucial.