Dark Pattern Detection Using
Bidirectional Long Short-Term Memory Networks

1.1 Defining Dark Patterns and Their Impact

Dark patterns are user interface (UI) design elements and interaction techniques that are intentionally crafted to mislead, coerce, or manipulate users into making choices that are often against their best interests or intentions [1]. These deceptive practices can range from subtle nudges to overtly confusing or misleading interfaces, and they are prevalent across various digital platforms.

The impact of dark patterns is multifaceted, leading to user frustration, privacy violations, unintended purchases, and a general erosion of trust in digital systems. For businesses, while dark patterns might offer short-term gains, they can damage long-term customer relationships and brand reputation.

1.2 Current Challenges in Detection

Detecting dark patterns presents significant challenges due to their diverse and evolving nature. One major challenge is the subtlety and variety of these patterns, which can make them difficult to define and identify systematically. Dark patterns often mimic legitimate design elements, blurring the line between poor usability and intentional deception.

Another challenge is the rapid evolution of these tactics; as users become aware of certain dark patterns, designers adapt and create new, more sophisticated ones [2]. Furthermore, the context-dependency of dark patterns means that an element might be manipulative in one context but acceptable in another.

1.3 The Potential of Deep Learning

Deep learning holds considerable promise for addressing the complexities of dark pattern detection. Unlike traditional rule-based systems, deep learning models can automatically learn hierarchical feature representations from raw or minimally processed data [3], [5].

Architectures like Convolutional Neural Networks (CNNs) can analyze visual elements and layouts, while Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks and their variants, are well-suited for processing sequential data like text or interaction flows.

2.1 Existing Methodologies

Current methodologies for identifying manipulative tactics can be broadly categorized into manual, rule-based, and machine learning approaches. Manual detection, often conducted by experts through heuristic evaluation or user studies, provides deep insights but lacks scalability [4].

Rule-based systems automate detection by defining specific criteria or patterns indicative of dark patterns. While effective for known patterns, they struggle with novelty and require extensive manual effort to maintain. More recently, machine learning, particularly deep learning, has emerged as a promising avenue for automating dark pattern detection with greater adaptability [3], [5].

2.2 Rule-Based and Heuristic Approaches

Rule-based and heuristic approaches rely on predefined sets of rules, often derived from established dark pattern taxonomies and expert knowledge. UIGuard, a system for mobile applications, utilizes computer vision and natural language pattern matching [14].

Similarly, AidUI employs rule-based techniques to determine the existence of dark patterns by analyzing UI elements, their attributes, and textual content [12]. While these methods can be precise in identifying specific, well-defined dark patterns, their major limitation lies in their rigidity and poor generalizability.

2.3 Machine Learning and Deep Learning Approaches

AI-powered auditing tools combine computer vision (CV) to analyze visual elements, natural language processing (NLP) to scrutinize text, and behavioral analysis to understand interaction paths [3], [5].

Research has explored transformer-based pre-trained language models like BERT, RoBERTa, ALBERT, and XLNet for detecting dark patterns from text in e-commerce websites, with RoBERTa achieving 0.975 accuracy [6], [15].

3.1 Dataset Description and Preprocessing

The foundation of any effective machine learning model is a robust and well-prepared dataset. For this research, the primary datasets utilized are pattern_classifications.csv and patterns_updated.csv, sourced from Kaggle input directories.

Critical data quality steps were undertaken: any rows with null "Pattern String" values were removed, duplicates were eliminated, and classifications were standardized to "Dark" and "Not Dark" categories. The combined dataset was shuffled to ensure no spurious order-based patterns were learned.

3.2 Text Cleaning and Tokenization

The text data underwent rigorous cleaning and transformation through a comprehensive clean_text function that:

• Converted all text to lowercase
• Removed numerical characters
• Eliminated non-alphanumeric characters
• Standardized whitespace

Tokenization was performed using TensorFlow's Tokenizer with a vocabulary size of 5000 words and maximum sequence length of 50 tokens, with the tokenizer and label encoder saved for consistent future preprocessing.

3.4 Model Training and Optimization

The Bi-LSTM model was compiled using the Adam optimizer and binary_crossentropy loss function. Several callbacks were employed to enhance training:

• EarlyStopping: Monitoring val_loss with patience of 5 epochs
• ReduceLROnPlateau: Reducing learning rate by factor of 0.3 with patience of 3 epochs

The model was trained for a maximum of 50 epochs with batch size of 32, with training stopping at epoch 15 due to early stopping criteria.

4.2 Training Dynamics

The training and validation curves provide valuable insights into the model's learning dynamics. Both training and validation loss decreased consistently and converged to low values without significant divergence, suggesting effective regularization and generalization.

The training accuracy began at 75.12% and rapidly increased to 99.54%, while validation accuracy showed a strong upward trend from 82.28% to 97.95%. The close tracking of validation accuracy with training accuracy further reinforces good generalization.

4.3 Comparative Analysis

The proposed Bi-LSTM model's test accuracy of 97.76% can be contextualized against related work in dark pattern detection:

The proposed model's performance is competitive with state-of-the-art transformer models and surpasses other BiLSTM implementations and multi-modal approaches in similar domains.

4.4 Strengths and Limitations

5.2 Implications for AI Research

The findings validate Bi-LSTM networks as a potent tool for text-based dark pattern detection, offering a viable alternative or complement to existing methods like transformer models or multi-modal approaches. The high accuracy achieved with a relatively straightforward deep learning architecture suggests that focused models can be highly effective.

For practitioners, this research provides a potential pathway for developing automated tools to scan and flag potentially deceptive textual content in user interfaces, thereby aiding in the enforcement of ethical design principles and protecting users.

5.3 Future Research Directions