Keywords

Accessibility, Dark Pattern, UI, UX, Web Browsing

Abstract

Dark patterns are deceptive interface designs that steer users toward unintended actions, often at financial or privacy cost. This thesis examines how those that are vision impaired are at greater risk of these dark patterns given their visual acuity deficit. We conducted semi structured interviews with nine participants across common contexts including e-commerce checkout, subscription and cancellation flows, software download pages, and mobile permission prompts. The data show recurring exposure to dark patterns such as hidden or de-emphasized fees, pre-selected add-ons, deceptive advertising and fake download buttons along with unequal consent paths, repeated permission nagging, and cancellation friction. Participants reported concrete harms, including unexpected charges and inflated totals, time and effort spent verifying or reversing choices, accessibility burden when critical information is difficult to perceive, and negative effects including annoyance, frustration, distrust, and feeling manipulated. We identify a coherent coping repertoire that users deploy based on when the issue is detected and how reversible the interface remains: vigilance (careful review of totals, checkboxes, and link destinations), undo in the moment (unchecking, removing, or revising before committing), stopping in the moment (abandoning the flow), clean up after the fact (unsubscribing and changing settings), and escalation (contacting support or relying on others when self-correction is blocked). Breakdowns in coping are linked to reduced visual clarity, obscured disclosures, and constrained reversibility, disproportionately affecting users with accessibility needs. We conclude with design implications for transparent, accessible, and reversible interfaces, emphasizing clear price disclosure, safe defaults, meaningful consent choices, and straightforward cancellation.

Completion Date

2026

Semester

Spring

Committee Chair

Yao Li

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Format

PDF

Document Type

Thesis

Identifier

DP0053147

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