Article: Legal framework development through adversarial transfer learning for consumer grievance classification in algorithmic price discrimination contexts Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.30 pp.81 - 96 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. Inderscience Publishers - linking academia, business and industry through research

Title: Legal framework development through adversarial transfer learning for consumer grievance classification in algorithmic price discrimination contexts

Authors: Xiaoling Guo

Addresses: School of Economics and Finance, Zhanjiang University of Science and Technology, Guangdong 524084, China

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.

Keywords: algorithmic price discrimination; complaint information categorisation; legal regulation; attention mechanisms; adversarial transfer learning.

DOI: 10.1504/IJICT.2025.147766

International Journal of Information and Communication Technology, 2025 Vol.26 No.30, pp.81 - 96

Received: 02 Jun 2025
Accepted: 16 Jun 2025
Published online: 30 Jul 2025 *

Title: Legal framework development through adversarial transfer learning for consumer grievance classification in algorithmic price discrimination contexts

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