VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.

Browsing by Author "Li, Toby"

Now showing 1 - 3 of 3
  • Thumbnail Image
    An Empathy-Based Sandbox Approach to Bridge the Privacy Gap among Attitudes, Goals, Knowledge, and Behaviors
    Chen, Chaoran; Li, Weijun; Song, Wenxin; Ye, Yanfang; Yao, Yaxing; Li, Toby (ACM, 2024-05-11)
    Managing privacy to reach privacy goals is challenging, as evidenced by the privacy attitude-behavior gap. Mitigating this discrepancy requires solutions that account for both system opaqueness and users’ hesitations in testing diferent privacy settings due to fears of unintended data exposure.We introduce an empathy-based approach that allows users to experience how privacy attributes may alter system outcomes in a risk-free sandbox environment from the perspective of artifcially generated personas. To generate realistic personas, we introduce a novel pipeline that augments the outputs of large language models (e.g., GPT-4) using few-shot learning, contextualization, and chain of thoughts. Our empirical studies demonstrated the adequate quality of generated personas and highlighted the changes in privacy-related applications (e.g., online advertising) caused by diferent personas. Furthermore, users demonstrated cognitive and emotional empathy towards the personas when interacting with our sandbox. We ofered design implications for downstream applications in improving user privacy literacy.
  • Thumbnail Image
    From Awareness to Action: Exploring End-User Empowerment Interventions for Dark Patterns in UX
    Lu, Yuwen; Zhang, Chao; Yang, Yuewen; Yao, Yaxing; Li, Toby (ACM, 2024-04-23)
    The study of UX dark patterns, i.e., UI designs that seek to manipulate user behaviors, often for the benefit of online services, has drawn significant attention in the CHI and CSCW communities in recent years. To complement previous studies in addressing dark patterns from (1) the designer’s perspective on education and advocacy for ethical designs; and (2) the policymaker’s perspective on new regulations, we propose an end-user-empowerment intervention approach that helps users (1) raise the awareness of dark patterns and understand their underlying design intents; (2) take actions to counter the effects of dark patterns using a web augmentation approach. Through a two-phase co-design study, including 5 co-design workshops (N=12) and a 2-week technology probe study (N=15), we reported findings on the understanding of users' needs, preferences, and challenges in handling dark patterns and investigated the feedback and reactions to users' awareness of and action on dark patterns being empowered in a realistic in-situ setting.
  • Thumbnail Image
    SHAI 2023: Workshop on Designing for Safety in Human-AI Interactions
    Goyal, Nitesh; Hong, Sungsoo Ray; Mandryk, Regan; Li, Toby; Luther, Kurt; Wang, Dakuo (ACM, 2023-03-27)
    Generative ML models present a novel opportunity for a wider group of societal members to engage with AI, imagine new use cases, and applications with an increasing ability to disseminate the outcomes of such endeavors to larger audiences. However, owing to the novelty and despite best intentions, inadvertent outcomes might accrue leading to harms, especially to marginalized groups in society. As this field of Human AI Interaction advances, academic/ industry researchers, and industry practitioners have an opportunity to brainstorm how to best utilize this new technology. Our workshop is aimed at such practitioners and researchers at the intersection of AI and HCI who are interested in collaboratively identifying challenges, and solutions to create safer outcomes with Generative ML models.