"I will never pay for this" Perception of fairness and factors affecting behaviour on 'pay-or-ok' models

• URL: http://arxiv.org/abs/2505.12892v4
• Date: Thu, 31 Jul 2025 08:48:02 GMT
• Title: "I will never pay for this" Perception of fairness and factors affecting behaviour on 'pay-or-ok' models
• Authors: Victor Morel, Farzaneh Karegar, Cristiana Santos,
• Abstract summary: This study examines users' perceptions of cookie paywalls, their judgments of fairness, and the conditions under which they might consider paying.<n> participants primarily viewed cookie paywalls as profit-driven, with fairness perceptions varying depending on factors such as the presence of a third option beyond consent or payment.<n>Findings raise concerns about economic exclusion, where privacy and data protection might end up becoming a privilege rather than fundamental rights.
• Score: 2.5944208050492183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rise of cookie paywalls ('pay-or-ok' models) has prompted growing debates around the right to privacy and data protection, monetisation, and the legitimacy of user consent. Despite their increasing use across sectors, limited research has explored how users perceive these models or what shapes their decisions to either consent to tracking or pay. To address this gap, we conducted four focus groups (n= 14) to examine users' perceptions of cookie paywalls, their judgments of fairness, and the conditions under which they might consider paying, alongside a legal analysis within the EU data protection legal framework. Participants primarily viewed cookie paywalls as profit-driven, with fairness perceptions varying depending on factors such as the presence of a third option beyond consent or payment, transparency of data practices, and the authenticity or exclusivity of the paid content. Participants voiced expectations for greater transparency, meaningful control over data collection, and less coercive alternatives, such as contextual advertising or "reject all" buttons. Although some conditions, including trusted providers, exclusive content, and reasonable pricing, could make participants consider paying, most expressed reluctance or unwillingness to do so. Crucially, our findings raise concerns about economic exclusion, where privacy and data protection might end up becoming a privilege rather than fundamental rights. Consent given under financial pressure may not meet the standard of being freely given, as required by the GDPR. To address these concerns, we recommend user-centred approaches that enhance transparency, reduce coercion, ensure the value of paid content, and explore inclusive alternatives. These measures are essential for supporting fairness, meaningful choice, and user autonomy in consent-driven digital environments.

Related papers

• Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness [1.1999555634662633]
  Anonymization techniques can make it more difficult to accurately identify individuals.<n>Group fairness metrics can be degraded by up to four orders of magnitude.<n>Individual fairness metrics tend to improve under stronger anonymization.<n>This study provides critical insights into the trade-offs between privacy, fairness, and utility.
  arXiv  Detail & Related papers  (2025-05-12T18:32:28Z)
• Insights from an experiment crowdsourcing data from thousands of US Amazon users: The importance of transparency, money, and data use [6.794366017852433]
  This paper shares an innovative approach to crowdsourcing user data to collect otherwise inaccessible Amazon purchase histories, spanning 5 years, from more than 5000 US users. We developed a data collection tool that prioritizes participant consent and includes an experimental study design. Experiment results (N=6325) reveal both monetary incentives and transparency can significantly increase data sharing.
  arXiv  Detail & Related papers  (2024-04-19T20:45:19Z)
• Incentivized Truthful Communication for Federated Bandits [61.759855777522255]
  We propose an incentive compatible (i.e., truthful) communication protocol, named Truth-FedBan. We show that Truth-FedBan still guarantees the sub-linear regret and communication cost without any overheads.
  arXiv  Detail & Related papers  (2024-02-07T00:23:20Z)
• Legitimate Interest is the New Consent -- Large-Scale Measurement and Legal Compliance of IAB Europe TCF Paywalls [2.5944208050492183]
  We study the prevalence of cookie paywalls on the top one million websites using an automatic crawler. We identify 431 cookie paywalls, all using the Transparency and Consent Framework (TCF) We observe that cookie paywalls extensively rely on legitimate interest legal basis systematically conflated with consent.
  arXiv  Detail & Related papers  (2023-09-20T20:24:52Z)
• 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)
• The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning [26.53734856637336]
  This paper puts forth an idea for a textitfair amount to compensate users for their data at a given privacy level based on an axiomatic definition of fairness. We also formulate a heterogeneous federated learning problem for the platform with privacy level options for users.
  arXiv  Detail & Related papers  (2023-01-30T23:51:03Z)
• Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest [70.02478301291264]
  Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse. Prior studies have used black-box methods to show that these algorithms can lead to biased or discriminatory outcomes. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation.
  arXiv  Detail & Related papers  (2022-07-18T17:32:35Z)
• Joint Multisided Exposure Fairness for Recommendation [76.75990595228666]
  This paper formalizes a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation.
  arXiv  Detail & Related papers  (2022-04-29T19:13:23Z)
• Trustworthy Transparency by Design [57.67333075002697]
  We propose a transparency framework for software design, incorporating research on user trust and experience. Our framework enables developing software that incorporates transparency in its design.
  arXiv  Detail & Related papers  (2021-03-19T12:34:01Z)
• Fairness, Welfare, and Equity in Personalized Pricing [88.9134799076718]
  We study the interplay of fairness, welfare, and equity considerations in personalized pricing based on customer features. We show the potential benefits of personalized pricing in two settings: pricing subsidies for an elective vaccine, and the effects of personalized interest rates on downstream outcomes in microcredit.
  arXiv  Detail & Related papers  (2020-12-21T01:01:56Z)

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.