mlsanityData to 7 Oct 2026

(Un)informed Consent: Studying GDPR Consent Notices in the Field

arXiv 1909.02638 ↗First posted 5 Sep 2019cs.HC cs.CYcited by 23

Not on the map. Why?

Abstract (arXiv)

Since the adoption of the General Data Protection Regulation (GDPR) in May 2018 more than 60 % of popular websites in Europe display cookie consent notices to their visitors. This has quickly led to users becoming fatigued with privacy notifications and contributed to the rise of both browser extensions that block these banners and demands for a solution that bundles consent across multiple websites or in the browser. In this work, we identify common properties of the graphical user interface of consent notices and conduct three experiments with more than 80,000 unique users on a German website to investigate the influence of notice position, type of choice, and content framing on consent. We find that users are more likely to interact with a notice shown in the lower (left) part of the screen. Given a binary choice, more users are willing to accept tracking compared to mechanisms that require them to allow cookie use for each category or company individually. We also show that the wide-spread practice of nudging has a large effect on the choices users make. Our experiments show that seemingly small implementation decisions can substantially impact whether and how people interact with consent notices. Our findings demonstrate the importance for regulation to not just require consent, but also provide clear requirements or guidance for how this consent has to be obtained in order to ensure that users can make free and informed choices.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Sep 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 4; 2024: 3; 2025: 5; 2026: 6.

23 papers.

Sort:

By area:

  1. 2024Stereotype or Personalization? User Identity Biases Chatbot Recommendationscited by 33
  2. 2023FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clientscited by 23
  3. 2024LLM Whisperer: An Inconspicuous Attack to Bias LLM Responsescited by 15
  4. 2025Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversightcited by 15
  5. 2024Trustworthy Distributed AI Systems: Robustness, Privacy, and Governancecited by 12
  6. 2023AidUI: Toward Automated Recognition of Dark Patterns in User Interfacescited by 9
  7. 2023FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusioncited by 8
  8. 2021AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Riskscited by 6
  9. 2022Regulating Gatekeeper AI and Data: Transparency, Access, and Fairness under the DMA, the GDPR, and beyondcited by 4
  10. 2025Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacycited by 4
  11. 2026Options, Not Clicks: Lattice Refinement for Consent-Driven MCP Authorizationcited by 4
  12. 2021A Decentralized Approach towards Responsible AI in Social Ecosystemscited by 3
11 more
  1. 2022Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other waycited by 3
  2. 2025Secondary Stakeholders in AI: Fighting for, Brokering, and Navigating Agencycited by 3
  3. 2026How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?cited by 3
  4. 2025Algorithmic Collective Action with Multiple Collectivescited by 2
  5. 2022NL2GDPR: Automatically Develop GDPR Compliant Android Application Features from Natural Languagecited by 0
  6. 2023Mapping the Empirical Evidence of the GDPR (In-)Effectiveness: A Systematic Reviewcited by 0
  7. 2025Using Salient Object Detection to Identify Manipulative Cookie Banners that Circumvent GDPRcited by 0
  8. 2026From Fragmentation to Integration: Exploring the Design Space of AI Agents for Human-as-the-Unit Privacy Managementcited by 0
  9. 2026On the Suitability of LLM-Driven Agents for Dark Pattern Auditscited by 0
  10. 2026A Statistical Framework for Algorithmic Collective Action with Multiple Collectivescited by 0
  11. 2026SoK: From Generation to Consumption of Privacy Documents in Software Systemscited by 0