Consumer Rights and Algorithms

• URL: http://arxiv.org/abs/2603.10022v1
• Date: Sun, 22 Feb 2026 14:19:44 GMT
• Title: Consumer Rights and Algorithms
• Abstract summary: This article summarizes the field of consumer protection law, from its historical roots to the contemporary challenges of the digital age.<n>It outlines the legal doctrines governing consumer deception and unfair trade practices, highlighting the interplay between common-law, statutory, and private modes of regulation.
• Score: 0.09444500584367876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article summarizes the field of consumer protection law, from its historical roots to the contemporary challenges of the digital age. It outlines the legal doctrines governing consumer deception and unfair trade practices, highlighting the interplay between common-law, statutory, and private modes of regulation. The article then addresses the impact of artificial intelligence and big data on consumer markets, focusing on digital advertising and new forms of consumer fraud. Finally, it explores regulatory responses to these challenges, including data privacy laws and prohibitions on dark patterns, which illustrate the trade-offs inherent in consumer protection frameworks.

Related papers

• The Perfect Match? A Closer Look at the Relationship between EU Consumer Law and Data Protection Law [0.8076386449963743]
  In modern markets, many companies offer so-called 'free' services and monetize consumer data they collect through those services.<n>This paper argues that consumer law and data protection law can usefully complement each other.
  arXiv  Detail & Related papers  (2025-10-15T12:13:35Z)
• Protecting Persona Biometric Data: The Case of Facial Privacy [0.0]
  This article introduces the concept of Facial Privacy.<n>It analyzes the profound challenges posed by unregulated facial recognition.<n>It argues that existing legal loopholes and ambiguities leave individuals vulnerable.
  arXiv  Detail & Related papers  (2025-10-03T14:16:33Z)
• The Patterns of Digital Deception [0.0]
  New data-analysis techniques have disrupted the balance of power between companies and their customers.<n>Online tracking enables sellers to amass troves of historical data, apply machine-learning tools to construct detailed customer profiles.<n>The same tools are also used for ill -- to target vulnerable members of the population with scams specially tailored to prey on their weaknesses.
  arXiv  Detail & Related papers  (2024-12-25T15:55:17Z)
• Copyright Protection in Generative AI: A Technical Perspective [58.84343394349887]
  Generative AI has witnessed rapid advancement in recent years, expanding their capabilities to create synthesized content such as text, images, audio, and code. The high fidelity and authenticity of contents generated by these Deep Generative Models (DGMs) have sparked significant copyright concerns. This work delves into this issue by providing a comprehensive overview of copyright protection from a technical perspective.
  arXiv  Detail & Related papers  (2024-02-04T04:00:33Z)
• SoK: The Gap Between Data Rights Ideals and Reality [42.769107967436945]
  Do rights-based privacy laws effectively empower individuals over their data?<n>This paper scrutinizes these approaches by reviewing empirical studies, news articles, and blog posts.
  arXiv  Detail & Related papers  (2023-12-03T21:52:51Z)
• Privately Policing Dark Patterns [0.0]
  Lawmakers around the country are crafting new laws to target "dark patterns" This Article proposes leveraging state private law to define and track dark patterns as they evolve.
  arXiv  Detail & Related papers  (2023-07-15T21:59:05Z)
• Ethical Considerations of AR Applications in Smartphones; A Systematic Literature Review of Consumer Perspectives [0.0]
  This study focuses on the ethical considerations that a consumer perceives with augmented reality (AR) in the context of smartphone applications. This research can provide an understanding and ability for developers, product managers, digital marketers and associated business professionals.
  arXiv  Detail & Related papers  (2023-06-06T11:22:58Z)
• Protecting User Privacy in Online Settings via Supervised Learning [69.38374877559423]
  We design an intelligent approach to online privacy protection that leverages supervised learning. By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user.
  arXiv  Detail & Related papers  (2023-04-06T05:20:16Z)
• Second layer data governance for permissioned blockchains: the privacy management challenge [58.720142291102135]
  In pandemic situations, such as the COVID-19 and Ebola outbreak, the action related to sharing health data is crucial to avoid the massive infection and decrease the number of deaths. In this sense, permissioned blockchain technology emerges to empower users to get their rights providing data ownership, transparency, and security through an immutable, unified, and distributed database ruled by smart contracts.
  arXiv  Detail & Related papers  (2020-10-22T13:19:38Z)
• Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding [53.02059906193556]
  We propose to predict consumers' purchases based on their facial features and purchasing histories. We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers. Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
  arXiv  Detail & Related papers  (2020-07-14T06:06:41Z)
• A vision for global privacy bridges: Technical and legal measures for international data markets [77.34726150561087]
  Despite data protection laws and an acknowledged right to privacy, trading personal information has become a business equated with "trading oil" An open conflict is arising between business demands for data and a desire for privacy. We propose and test a vision of a personal information market with privacy.
  arXiv  Detail & Related papers  (2020-05-13T13:55:50Z)
• Beyond privacy regulations: an ethical approach to data usage in transportation [64.86110095869176]
  We describe how Federated Machine Learning can be applied to the transportation sector. We see Federated Learning as a method that enables us to process privacy-sensitive data, while respecting customer's privacy.
  arXiv  Detail & Related papers  (2020-04-01T15:10:12Z)

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.