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"What I'm Interested in is Something that Violates the Law": Regulatory Practitioner Views on Automated Detection of Deceptive Design Patterns

Arianna Rossi, Simon Parkin

TL;DR

Deceptive design patterns pose regulatory challenges at scale, motivating automated detection as a potential enabler for EnfTech. Through interviews with nine EU practitioners, the paper reveals that enforcement relies on solid evidence of legal violations and human-centric interpretation, limiting the direct transfer of academic DP-detection tools into practice. The findings emphasize the need for user-centered requirements, mapping of design patterns to legal provisions, and practical adoption pathways, including open-source collaborations and co-design spaces. The work offers concrete guidance for aligning research with enforcement workflows, improving transparency, traceability, and collaboration between regulators and academia to advance digital fairness.

Abstract

Although deceptive design patterns are subject to growing regulatory oversight, enforcement races to keep up with the scale of the problem. One promising solution is automated detection tools, many of which are developed within academia. We interviewed nine experienced practitioners working within or alongside regulatory bodies to understand their work against deceptive design patterns, including the use of supporting tools and the prospect of automation. Computing technologies have their place in regulatory practice, but not as envisioned in research. For example, investigations require utmost transparency and accountability in all the activities we identify as accompanying dark pattern detection, which many existing tools cannot provide. Moreover, tools need to map interfaces to legal violations to be of use. We thus recommend conducting user requirement research to maximize research impact, supporting ancillary activities beyond detection, and establishing practical tech adoption pathways that account for the needs of both scientific and regulatory activities.

"What I'm Interested in is Something that Violates the Law": Regulatory Practitioner Views on Automated Detection of Deceptive Design Patterns

TL;DR

Deceptive design patterns pose regulatory challenges at scale, motivating automated detection as a potential enabler for EnfTech. Through interviews with nine EU practitioners, the paper reveals that enforcement relies on solid evidence of legal violations and human-centric interpretation, limiting the direct transfer of academic DP-detection tools into practice. The findings emphasize the need for user-centered requirements, mapping of design patterns to legal provisions, and practical adoption pathways, including open-source collaborations and co-design spaces. The work offers concrete guidance for aligning research with enforcement workflows, improving transparency, traceability, and collaboration between regulators and academia to advance digital fairness.

Abstract

Although deceptive design patterns are subject to growing regulatory oversight, enforcement races to keep up with the scale of the problem. One promising solution is automated detection tools, many of which are developed within academia. We interviewed nine experienced practitioners working within or alongside regulatory bodies to understand their work against deceptive design patterns, including the use of supporting tools and the prospect of automation. Computing technologies have their place in regulatory practice, but not as envisioned in research. For example, investigations require utmost transparency and accountability in all the activities we identify as accompanying dark pattern detection, which many existing tools cannot provide. Moreover, tools need to map interfaces to legal violations to be of use. We thus recommend conducting user requirement research to maximize research impact, supporting ancillary activities beyond detection, and establishing practical tech adoption pathways that account for the needs of both scientific and regulatory activities.
Paper Structure (57 sections, 2 figures, 3 tables)

This paper contains 57 sections, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Defining and categorizing DPs.
  4. Automated DP detection
  5. Deceptive design and the law
  6. Automated detection of legal violations
  7. Actors in the detection and enforcement activities
  8. Latent challenges
  9. Methodology
  10. Interview study design
  11. Recruitment and participants
  12. Interview data analysis
  13. Analysis of existing AI-based computing technologies
  14. Ethics, privacy and confidentiality
  15. Limitations
  16. ...and 42 more sections

Figures (2)

  • Figure 1: The phases reflecting the activities of enforcement identified in the interviews. A shorthand definition is provided for each phase on the diagram's right-hand side, and an appropriate quote on the left-hand side. On the far right is a fictional example of what the enforcement process could look like, inspired by the activities described by our participants.
  • Figure 2: The overview of the three types of tools summarized to participants during the interviews: Hall of shame, Machine Learning (ML), and Large Language Model (LLM).