Hidden Darkness in LLM-Generated Designs: Exploring Dark Patterns in Ecommerce Web Components Generated by LLMs

• URL: http://arxiv.org/abs/2502.13499v1
• Date: Wed, 19 Feb 2025 07:35:07 GMT
• Title: Hidden Darkness in LLM-Generated Designs: Exploring Dark Patterns in Ecommerce Web Components Generated by LLMs
• Authors: Ziwei Chen, Jiawen Shen, Luna, Kristen Vaccaro,
• Abstract summary: This work evaluated designs of ecommerce web components generated by four popular LLMs: Claude, GPT, Gemini, and Llama. Over one-third of generated components contain at least one dark pattern. Dark patterns are also more frequently produced in components that are related to company interests.
• Score: 4.934936297965669
• License:
• Abstract: Recent work has highlighted the risks of LLM-generated content for a wide range of harmful behaviors, including incorrect and harmful code. In this work, we extend this by studying whether LLM-generated web design contains dark patterns. This work evaluated designs of ecommerce web components generated by four popular LLMs: Claude, GPT, Gemini, and Llama. We tested 13 commonly used ecommerce components (e.g., search, product reviews) and used them as prompts to generate a total of 312 components across all models. Over one-third of generated components contain at least one dark pattern. The majority of dark pattern strategies involve hiding crucial information, limiting users' actions, and manipulating them into making decisions through a sense of urgency. Dark patterns are also more frequently produced in components that are related to company interests. These findings highlight the need for interventions to prevent dark patterns during front-end code generation with LLMs and emphasize the importance of expanding ethical design education to a broader audience.

Related papers

• Do Code LLMs Understand Design Patterns? [45.89136944351375]
  We empirically investigate the biases of Code LLMs in software development. Our findings reveal that biases in Code LLMs significantly affect the reliability of downstream tasks.
  arXiv  Detail & Related papers  (2025-01-08T20:39:45Z)
• Fine-tuning Multimodal Large Language Models for Product Bundling [53.01642741096356]
  We introduce Bundle-MLLM, a novel framework that fine-tunes large language models (LLMs) through a hybrid item tokenization approach. Specifically, we integrate textual, media, and relational data into a unified tokenization, introducing a soft separation token to distinguish between textual and non-textual tokens. We propose a progressive optimization strategy that fine-tunes LLMs for disentangled objectives: 1) learning bundle patterns and 2) enhancing multimodal semantic understanding specific to product bundling.
  arXiv  Detail & Related papers  (2024-07-16T13:30:14Z)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
  Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions. We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types. We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation [58.04939553630209]
  In real-world systems, most users interact with only a handful of items, while the majority of items are seldom consumed. These two issues, known as the long-tail user and long-tail item challenges, often pose difficulties for existing Sequential Recommendation systems. We propose the Large Language Models Enhancement framework for Sequential Recommendation (LLM-ESR) to address these challenges.
  arXiv  Detail & Related papers  (2024-05-31T07:24:42Z)
• Detecting Deceptive Dark Patterns in E-commerce Platforms [0.0]
  Dark patterns are deceptive user interfaces employed by e-commerce websites to manipulate user's behavior in a way that benefits the website, often unethically. Existing solutions include UIGuard, which uses computer vision and natural language processing, and approaches that categorize dark patterns based on detectability or utilize machine learning models trained on datasets. We propose combining web scraping techniques with fine-tuned BERT language models and generative capabilities to identify dark patterns, including outliers.
  arXiv  Detail & Related papers  (2024-05-27T16:32:40Z)
• Integrating Dark Pattern Taxonomies [0.0]
  Malicious and explotitative design has expanded to multiple domains in the past 10 years. By leaning on network analysis tools and methods, this paper synthesizes existing elements through as a directed graph. In doing so, the interconnectedness of Dark patterns can be more clearly revealed via community detection.
  arXiv  Detail & Related papers  (2024-02-26T17:26:31Z)
• Why is the User Interface a Dark Pattern? : Explainable Auto-Detection and its Analysis [1.4474137122906163]
  Dark patterns are deceptive user interface designs for online services that make users behave in unintended ways. We study interpretable dark pattern auto-detection, that is, why a particular user interface is detected as having dark patterns. Our findings may prevent users from being manipulated by dark patterns, and aid in the construction of more equitable internet services.
  arXiv  Detail & Related papers  (2023-12-30T03:53:58Z)
• A Survey on Detection of LLMs-Generated Content [97.87912800179531]
  The ability to detect LLMs-generated content has become of paramount importance. We aim to provide a detailed overview of existing detection strategies and benchmarks. We also posit the necessity for a multi-faceted approach to defend against various attacks.
  arXiv  Detail & Related papers  (2023-10-24T09:10:26Z)
• On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused? [49.99955642001019]
  We show that open-sourced, aligned large language models could be easily misguided to generate undesired content. Our key idea is to directly manipulate the generation process of open-sourced LLMs to misguide it to generate undesired content.
  arXiv  Detail & Related papers  (2023-10-02T19:22:01Z)
• Getting Trapped in Amazon's "Iliad Flow": A Foundation for the Temporal Analysis of Dark Patterns [17.59481743387609]
  We present a case study of Amazon Prime's "Iliad Flow" to illustrate the interplay of dark patterns across a user journey. We use this case study to lay the groundwork for a methodology of Temporal Analysis of Dark Patterns (TADP)
  arXiv  Detail & Related papers  (2023-09-18T10:12:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.