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From Prompt to Service: An SLM-Based Agent Orchestration Gateway for AI-Driven Virtual Worlds What LLMs Must Forget to Teach Effectively: A DIY Approach to Premodern Japanese Language Pedagogy Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
Simply tell me how -- On Trustworthiness and Technology A...
Rachel Crowder, George Price, Thomas Groß · 2023-08-12 · via cs.HC updates on arXiv.org

Attribute-based Credential Systems (ACS) have been long proposed as privacy-preserving means of attribute-based authentication, yet neither been considered particularly usable nor found wide-spread adoption, to date. To establish what variables drive the adoption of \ACS as a usable security and privacy technology, we investigated how intrinsic and presentation properties impact their perceived trustworthiness and behavioral intent to adopt them. We conducted two confirmatory, fractional-factorial, between-subject, random-controlled trials with a total UK-representative sample of $N = 812$ participants. Each participant inspected one of 24 variants of Anonymous Credential System Web site, which encoded a combination of three intrinsic factors (\textsf{provider}, \textsf{usage}, \textsf{benefits}) and three presentation factors (\textsf{simplicity}, presence of \textsf{people}, level of available \textsf{support}). Participants stated their privacy and faith-in-technology subjective norms before the trial. After having completed the Web site inspection, they reported on the perceived trustworthiness, the technology adoption readiness, and their behavioral intention to follow through. We established a robust covariance-based structural equation model of the perceived trustworthiness and technology acceptance, showing that communicating facilitating conditions as well as demonstrating results drive the overall acceptance and behavioral intent. Of the manipulated causal variables, communicating with simplicity and on the everyday usage had the greatest and most consistently positive impact on the overall technology acceptance. After earlier correlational empirical research on ACS technology acceptance, ours is the first research showing cause-effect relations in a structural latent factor model with substantial sample size.