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. This study investigates the detection of such dark patterns.
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. The
approach scrapes textual content, feeds it into the BERT model for detection,
and leverages BERT's bidirectional analysis and generation abilities. The study
builds upon research on automatically detecting and explaining dark patterns,
aiming to raise awareness and protect consumers.

通过利用 web 抓取技术与细调的 BERT 语言模型和生成能力相结合，检测并解释欺骗性用户界面中的暗模式，提升公众意识和保护消费者。

电子商务平台中的欺骗性黑模式检测

Detecting Deceptive Dark Patterns in E-commerce Platforms

Recent work has demonstrated that natural language processing techniques can
support consumer protection by automatically detecting unfair clauses in the
Terms of Service (ToS) Agreement. This work demonstrates that transformer-based
ToS analysis systems are vulnerable to adversarial attacks. We conduct
experiments attacking an unfair-clause detector with universal adversarial
triggers. Experiments show that a minor perturbation of the text can
considerably reduce the detection performance. Moreover, to measure the
detectability of the triggers, we conduct a detailed human evaluation study by
collecting both answer accuracy and response time from the participants. The
results show that the naturalness of the triggers remains key to tricking
readers.

本文研究证明基于 Transformer 的服务条款分析系统容易受到对抗攻击，微小的文本扰动可以显著降低检测性能。此外，人类评估研究表明触发器的自然性对欺骗读者至关重要。

针对不公平 ToS 条款检测的攻击：一个使用通用对抗性触发器的案例研究

Attack on Unfair ToS Clause Detection: A Case Study using Universal Adversarial Triggers

The widespread availability of behavioral data has led to the development of
data-driven personalized pricing algorithms: sellers attempt to maximize their
revenue by estimating the consumer's willingness-to-pay and pricing
accordingly. Our objective is to develop algorithms that protect consumer
interests against personalized pricing schemes. In this paper, we consider a
consumer who learns more and more about a potential purchase across time, while
simultaneously revealing more and more information about herself to a potential
seller. We formalize a strategic consumer's purchasing decision when
interacting with a seller who uses personalized pricing algorithms, and
contextualize this problem among the existing literature in optimal stopping
time theory and computational finance. We provide an algorithm that consumers
can use to protect their own interests against personalized pricing algorithms.
This algorithmic stopping method uses sample paths to train estimates of the
optimal stopping time. To the best of our knowledge, this is one of the first
works that provides computational methods for the consumer to maximize her
utility when decision making under surveillance. We demonstrate the efficacy of
the algorithmic stopping method using a numerical simulation, where the seller
uses a Kalman filter to approximate the consumer's valuation and sets prices
based on myopic expected revenue maximization. Compared to a myopic purchasing
strategy, we demonstrate increased payoffs for the consumer in expectation.

针对个性化定价情况，提出了一种消费者保护策略 —— 利用基于样本路径训练最优停止时间的算法停止决策，实现对决策的最大效益，同时也是首个提供了消费者在监控行为下利用计算方法最大化效益的方法之一。