Tackling Dark Pattern-Induced Online Deception of People with Visual Disabilities
Researchers will plug a gap in dark-pattern research by developing counter-measures to mitigate deceptive patterns on websites that are specific to blind and low vision (BLV) users who use assistive technologies.
Funded by the CCI Coastal Virginia Node
Project Investigators
- Principal Investigator (PI): Vikas Ashok, Old Dominion University (ODU) Department of Computer Science
- Co-PI: Faryaneh Poursardar, ODU’s Virginia Modeling and Simulation Center (VMASC)
Rationale and Background
Deceptive dark patterns, such as interface designs that manipulate, coerce, or deceive web users into doing something, are commonplace. BLV users also interact with web content via screen readers and magnifiers, experiencing disproportionate effects on privacy and security. Research has focused on sighted interaction, with little known about dark patterns that BLV people encounter, and how these patterns affect the user experience.
Methodology
The project will be conducted in two stages:
- Uncovering different types of web dark-pattern designs that deceive BLV individuals. Researchers will conduct an Institutional Review Board-approved interview study with 50 diverse sets of BLV participants. To recruit participants, researchers will get assistance from ODU's Office of Educational Accessibility as well as the Lighthouse Guild in New York City.
- Building a representative dataset containing BLV-specific dark-pattern examples from a wide range of popular websites focused on such topics as shopping, travel, entertainment, sports, news, and social media.
Projected Outcomes
Researchers will:
- Develop a taxonomy of dark patterns encountered by BLV persons on the web.
- Establish an in-depth understanding of the financial, cognitive, temporal, and privacy effects from online dark patterns on BLV persons.
- Share intermediate results via X (Twitter) and blogs.
- Disseminate study findings through publications and sites such as GitHub.