Authors

Tarilatei Mao Claudius

Department of Computer Science, Rivers State University, Port-Harcourt, Nigeria

N. D. Nwiabu

Department of Computer Science, Rivers State University, Port-Harcourt, Nigeria

C. G. Igiri

Department of Computer Science, Rivers State University, Port-Harcourt, Nigeria

Abstract

Dark patterns are manipulative interface designs that steer users toward unintended purchases, subscriptions, or disclosures, and they are widespread across e-commerce platforms. Existing automated detection approaches are largely single-modality and offer limited transparency into why a pattern was flagged, which restricts their use by regulators, platform developers, and consumers. This study designed, implemented, and evaluated DPNet-v2.4.1-XAI, an explainable multi-modal detection system that fuses visual (CNN), textual (BERT), and DOM-structural signals and pairs them with a dual SHAP and Grad-CAM explainability layer. The model was trained on 5,200 labelled samples across eight dark-pattern categories (Urgency, Scarcity, Misdirection, Social Proof, Forced Action, Obstruction, Sneaking, and Confirmshaming) and delivered as a six-module web application. On a held-out test set of 520 e-commerce interface samples it achieved 92.3% accuracy and an F1-score of 89.9%, outperforming the benchmark system of Vedhapriyavadhana et al. (2025) by 3.2 and 2.5 percentage points on accuracy and F1-score respectively, and by 8.0 percentage points on explainability faithfulness (87.0% versus 79.0%). Field testing on 18 live Nigerian and cross-border e-commerce pages detected 98 pattern instances, with confidence scores as high as 93%. The results demonstrate that multi-modal fusion measurably improves both detection performance and explanation faithfulness over a single-modality baseline, offering a deployable, transparent tool for consumers, platform developers, and regulators seeking to identify manipulative design at the point of interaction.

Keywords

Dark Patterns Explainable AI E-Commerce Multi-Modal Deep Learning SHAP Grad-CAM Consumer Protection Web Application

Citation of this Article

Tarilatei Mao Claudius, N. D. Nwiabu, & C. G. Igiri. (2026). An Explainable Web-Based Model to Identify Dark Pattern on E-Commerce User Interfaces. Journal of Artificial Intelligence and Emerging Technologies (JAIET). 3(10), 14-24. Article DOI: https://doi.org/10.47001/JAIET/2026.310003 

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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