TourMart quantifies commission steering in LLM travel agents via paired counterfactual prompts, reporting 3.5-7.7 percentage point increases in steered recommendations for tested models.
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9 Pith papers cite this work, alongside 514 external citations. Polarity classification is still indexing.
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Agentic browsers are vulnerable to 20 web and LLM attacks with 18 implemented, exposing five failure modes across four major LLM models that require redesign before safe deployment.
Privy uses LLMs to extract privacy rights from policies and deliver interactive guidance, reaching 0.979 precision and completing 96.3% of tasks in 3.2 steps on average across 14 sites.
ConsentDiff enables longitudinal tracking of privacy policy churn and consent UI patterns, finding ongoing changes, shifts away from high-friction banners, and higher policy-UI alignment when rejection options are visible.
Interviews with 12 privacy-advocating UI/UX designers reveal how personal values, team negotiations, and business pressures shape their efforts to implement privacy beyond legal minimums.
GreenZ is a conceptual three-layer sustainable UX framework built on ten principles, five operational systems, and practical tools, centered on an eight-type Digital Waste Taxonomy and a model questioning AI necessity before implementation.
Users' memory of privacy settings drifts over time from exact recall to gist-based impressions that bias toward sharing with larger audiences than originally intended.
WCAG guidelines flag three deceptive patterns—countdown timers, auto-play, and hidden information—as violations, providing a legal and design route to limit manipulative interfaces.
Deception in generative AI is subtle and normalized through defaults and interactions, with users often complicit, calling for friction, awareness, and regulatory approaches to protect users.
citing papers explorer
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TourMart: A Parametric Audit Instrument for Commission Steering in LLM Travel Agents
TourMart quantifies commission steering in LLM travel agents via paired counterfactual prompts, reporting 3.5-7.7 percentage point increases in steered recommendations for tested models.
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WAAA! Web Adversaries Against Agentic Browsers
Agentic browsers are vulnerable to 20 web and LLM attacks with 18 implemented, exposing five failure modes across four major LLM models that require redesign before safe deployment.
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Privy: From Fine Print to Fair Practice in Privacy Rights Exercise
Privy uses LLMs to extract privacy rights from policies and deliver interactive guidance, reaching 0.979 precision and completing 96.3% of tasks in 3.2 steps on average across 14 sites.
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ConsentDiff at Scale: Longitudinal Audits of Web Privacy Policy Changes and UI Frictions
ConsentDiff enables longitudinal tracking of privacy policy churn and consent UI patterns, finding ongoing changes, shifts away from high-friction banners, and higher policy-UI alignment when rejection options are visible.
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"We Wanted to Do Better Than the Law": Exploring UI/UX Designers' Privacy Advocacy in Practice
Interviews with 12 privacy-advocating UI/UX designers reveal how personal values, team negotiations, and business pressures shape their efforts to implement privacy beyond legal minimums.
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GreenZ: A Sustainable UX Framework for Complex Digital Systems
GreenZ is a conceptual three-layer sustainable UX framework built on ten principles, five operational systems, and practical tools, centered on an eight-type Digital Waste Taxonomy and a model questioning AI necessity before implementation.
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Temporal Drift in Privacy Recall: Users Misremember From Verbatim Loss to Gist-Based Overexposure
Users' memory of privacy settings drifts over time from exact recall to gist-based impressions that bias toward sharing with larger audiences than originally intended.
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Access Over Deception: Fighting Deceptive Patterns through Accessibility
WCAG guidelines flag three deceptive patterns—countdown timers, auto-play, and hidden information—as violations, providing a legal and design route to limit manipulative interfaces.
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Exploring the "Banality" of Deception in Generative AI
Deception in generative AI is subtle and normalized through defaults and interactions, with users often complicit, calling for friction, awareness, and regulatory approaches to protect users.