HCMar 4

A Systematic Review of User Experiments Measuring the Effects of Dark Patterns

arXiv:2604.15323h-index: 32
Originality Synthesis-oriented
AI Analysis

It addresses the need for empirical evidence on dark patterns' real-world effects to inform research and policy, but is incremental as it reviews existing studies.

This review synthesizes experimental studies on dark patterns, finding that they significantly alter user behavior with large variance in effect sizes, and interventions to mitigate these effects have been mostly unsuccessful.

Deceptive/Manipulative Patterns (DMP) are interface designs, also known as ``dark patterns,'' that manipulate user behavior. While considerable attention has been paid to their ethical and legal implications, empirical evidence about their real-world effects remains diffuse. This review synthesizes up-to-date experimental studies, focusing on works that quantify how (or whether) DMPs influence users. We also aggregate findings on interventions aimed at reducing DMP effects. Our synthesis highlights the experimental agreement that DMPs do significantly alter user behavior (with large variance in effect size) and that external interventions have been mostly unsuccessful in mitigating their effects. Lastly, we show that significant correlations between DMP effects and personal characteristics (e.g., age or political affiliation) are uncommon, indicating DMPs similarly affected nearly all populations tested. By summarizing the experimental evidence, we clarify the effects of DMPs, highlight gaps and tensions in the existing experimental literature, and help inform ongoing research and policy directions.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes