Hae-Na Lee
Michigan State University, MI·Computer Science
Many websites use manipulative layouts and wording, often called dark patterns, that nudge people into unintended purchases, sign-ups, or sharing personal data. People who are blind or have low vision usually browse with screen readers, software that reads a page aloud in sequence, and for them these tricks are especially hard to catch because visual and spatial cues are missing or misleading.
- Fields
Computer Science
Engineering
- Topics
dark patterns
deceptive design
web accessibility
- Grants
- 1 known grant, through Sep 2029
What they work on
- Develop detection algorithms that analyze webpage layouts, interaction workflows, user actions, and accessible navigation paths to identify deceptive design patterns, including ones created by mismatches between a page's visual structure and its screen reader reading order.
- Design customizable nonvisual assistive interfaces that let users understand and manage detected deceptive patterns, with requirements and refinements drawn from iterative studies with people with visual impairments.
- Integrate detection and interface components into a browser extension and evaluate real-world effectiveness, usability, privacy implications, cost, and latency through controlled experiments and user studies.
- Test how intelligent agents driven by large language models can model screen reader navigation behavior to explain the risks of a deceptive pattern and support informed decisions under real-world time and cost limits.
More about the research
This project asks how artificial intelligence can spot such deceptive designs and help screen reader users respond safely.
Why it matters
Screen reader users could face fewer unintended purchases and privacy losses, and spend less effort untangling confusing pages during everyday tasks like banking, healthcare, or shopping online. The detection methods and interface designs could also inform more transparent web design practices and tools that protect users across the broader web.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Jenna Gorlewicz
Saint Louis University, MO·Engineering
Build the core content platform for Inclusio, a system that takes graphics in many data formats and outputs them to web and mobile apps, braille and tactile embossers, multimodal touchpads, and refreshable tactile displays.
Xiaojun Bi
Stony Brook University, NY·Computer Science
Create a non-visual free-form gesture input method that lets blind users enter whole words or phrases by tracing continuous gestures on the standard Qwerty touchscreen keyboard, removing the need to confirm each letter.
Nicholas A Giudice
University of Maine, ME·Computer Science
Develop user-centered design guidelines that specify which information and sensory pairings, such as speech, sound or touch, best support the most common navigation tasks across a whole trip for blind and visually impaired travelers.
The school
Michigan State University: 28 labs on PI Pages, $932M a year on research, 555 research doctorates a year.
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