Projects per year
Abstract
Deceptive techniques known as dark patterns specifically target online users. Children are particularly vulnerable as they might lack the skills to recognise and resist these deceptive attempts. To be effective, interventions to forewarn and forearm should build on a comprehensive understanding of children’s existing mental models. To this end, we carried out a study with 11- to 12-year-old Scottish children to reveal their mental models of dark patterns. They were acutely aware of online deception, referring to deployers as being ‘up to no good.’ Yet, they were overly vigilant and construed worst-case outcomes, with even a benign warning triggering suspicion. We recommend that rather than focusing on specific instances of dark patterns in awareness raising, interventions should prioritise improving children’s understanding of the characteristics of, and the motivations behind, deceptive online techniques. By so doing, we can help them to develop a more robust defence against these deceptive practices.
Original language | English |
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Article number | 33 |
Pages (from-to) | 1-41 |
Number of pages | 41 |
Journal | ACM Transactions on Computer-Human Interaction |
Volume | 31 |
Issue number | 3 |
DOIs | |
Publication status | Published - 30 Aug 2024 |
Funding
We thank SPRITE (EP/S035869/1) and REPHRAIN (EP/W032473/1) for funding this research.
Keywords
- dark patterns
- online user threat
- Scottish children
- online deception
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- 1 Finished
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Addressing Privacy Risks and Legal Challenges in the Emergent Use of Text-based Generative AI for UK Research (REPHRAIN)
Moncur, W. (Principal Investigator), Farooq, A. (Co-investigator), Farooq, A. (Co-investigator), Gibson, R. C. (Research Co-investigator) & Gibson, R. C. (Research Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/11/23 → 31/08/24
Project: Research