PrivacyMotiv: Speculative Persona Journeys for Empathic and Motivating Privacy Reviews in UX Design

2025-10-03

AI Summary

Privacy concerns are frequently overlooked in UX design reviews, primarily due to practitioners’ limited privacy literacy, insufficient empathic engagement, and low confidence in identifying privacy risks. Method: We propose an LLM-driven privacy-enhanced design diagnosis method that generates speculative personas—centered on vulnerable populations—and narrative-based user journey maps. This story-driven approach fosters empathy and intrinsic motivation, addressing the narrow scope and shallow reflection on privacy in conventional reviews. The method integrates large language models, speculative design, journey mapping, and narrative construction. Contribution/Results: In a controlled study with 16 professional UX practitioners, the method significantly improved empathy (p < 0.01), intrinsic motivation (p < 0.05), and perceived tool utility (Cohen’s d = 0.92). It offers a scalable, actionable intervention paradigm for privacy-sensitive design practice.

Technology Category

• Machine Learning: Privacy
• Humans and AI: User Experience and Usability
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

UX professionals routinely conduct design reviews, yet privacy concerns are often overlooked -- not only due to limited tools, but more critically because of low intrinsic motivation. Limited privacy knowledge, weak empathy for unexpectedly affected users, and low confidence in identifying harms make it difficult to address risks. We present PrivacyMotiv, an LLM-powered system that supports privacy-oriented design diagnosis by generating speculative personas with UX user journeys centered on individuals vulnerable to privacy risks. Drawing on narrative strategies, the system constructs relatable and attention-drawing scenarios that show how ordinary design choices may cause unintended harms, expanding the scope of privacy reflection in UX. In a within-subjects study with professional UX practitioners (N=16), we compared participants' self-proposed methods with PrivacyMotiv across two privacy review tasks. Results show significant improvements in empathy, intrinsic motivation, and perceived usefulness. This work contributes a promising privacy review approach which addresses the motivational barriers in privacy-aware UX.

Problem

• Addressing low motivation in privacy reviews during UX design processes
• Improving empathy for vulnerable users through speculative persona journeys
• Enhancing identification of unintended privacy harms in design choices

Innovation

• Generates speculative personas for privacy risk scenarios
• Uses narrative strategies to create relatable harm scenarios
• LLM-powered system enhances empathy in UX design reviews