Legal framework development through adversarial transfer learning for consumer grievance classification in algorithmic price discrimination contexts
Year: 2025
Type: article
Abstract: The escalation of algorithmic price discrimination necessitates systematic analysis of consumer grievance data. Addressing the scarcity of annotated datasets in regulatory research, this study proposes a hybrid neural framework integrating BERT-based semantic encoding with optimised convolutional architectures. The model employs bidirectional recurrent layers to capture sequential dependencies while applying multi-head attention mechanisms for contextual feature fusion. A domain adaptation strategy combining adversarial training and transfer learning bridges feature distribution gaps between source and target domains. Through iterative parameter optimisation, the framework achieves cross-domain knowledge transfer while maintaining discriminative classification capabilities. Empirical validation demonstrates significant performance improvements, with macro-F1 scores increasing by 13.29% compared to baseline models. The classification outcomes inform three regulatory proposals addressing dynamic pricing oversight, algorithmic transparency requirements, and consumer compensation mechanisms. This dual technical-legal approach provides implementable solutions for governing emerging digital market practices while advancing domain adaptation methodologies in computational legal studies.
Author: Xiaoling Guo
Institution: Zhanjiang University of Science and Technology
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4412784567