# Why is the User Interface a Dark Pattern? : Explainable Auto-Detection and its Analysis

arXiv: https://arxiv.org/abs/2401.04119 · HTML: https://mlsanity.com/paper/2401.04119/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 30 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence](https://mlsanity.com/paper/2001.02479/) (2020)
- [Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites](https://mlsanity.com/paper/1907.07032/) (2019)
- [What Makes a Dark Pattern... Dark? Design Attributes, Normative Considerations, and Measurement Methods](https://mlsanity.com/paper/2101.04843/) (2021)
- [AidUI: Toward Automated Recognition of Dark Patterns in User Interfaces](https://mlsanity.com/paper/2303.06782/) (2023)
- [Circumvention by design -- dark patterns in cookie consents for online news outlets](https://mlsanity.com/paper/2006.13985/) (2020)
- [Dark patterns in e-commerce: a dataset and its baseline evaluations](https://mlsanity.com/paper/2211.06543/) (2022)
- [Linguistic Dead-Ends and Alphabet Soup: Finding Dark Patterns in Japanese Apps](https://mlsanity.com/paper/2304.12811/) (2023)

## Abstract (arXiv)

Dark patterns are deceptive user interface designs for online services that make users behave in unintended ways. Dark patterns, such as privacy invasion, financial loss, and emotional distress, can harm users. These issues have been the subject of considerable debate in recent years. In this paper, we study interpretable dark pattern auto-detection, that is, why a particular user interface is detected as having dark patterns. First, we trained a model using transformer-based pre-trained language models, BERT, on a text-based dataset for the automatic detection of dark patterns in e-commerce. Then, we applied post-hoc explanation techniques, including local interpretable model agnostic explanation (LIME) and Shapley additive explanations (SHAP), to the trained model, which revealed which terms influence each prediction as a dark pattern. In addition, we extracted and analyzed terms that affected the dark patterns. Our findings may prevent users from being manipulated by dark patterns, and aid in the construction of more equitable internet services. Our code is available at https://github.com/yamanalab/why-darkpattern.