Deceptive design patterns (also known as 'dark patterns') are design elements that trick users into unintended behaviors that harm them and benefit the party implementing the design. Prior work has taxonomized, investigated, and speculated the presence of dark patterns across environments (applications, websites, games, VR, AR, IoT) and modalities (visual, audio). However, more attention is needed to investigate how haptic technologies might be used to enable deceptive patterns since it is gradually becoming prevalent in digital experiences.
This project contributes to the characterization and demonstration of deceptive patterns enabled / augmented by haptic stimulation. First, we identify the key characteristics of haptic modality that may enable new dark patterns or argument existing ones, and we surface examples of such patterns. Second, we conducted a between-group study ,in which we investigated the efficacy of one chosen haptic dark pattern. In addition, we presented mitigative recommendations to the stakeholders.

The spread of misinformation on social media has become a prevalent issue, and emerging AI technology further accerlates the generation of misinformation. In this study, we investigate how humans perceive AI-generated and human-written news differently and whether they can distinguish between the two. We conducted an experiment that asked participants to evaluate a news dataset consisting of 16 articles of different authenticity (True or Fake) and origin (Human or AI-generated). Physiological signals, including gaze and heart rate data were captured during the study for analysis. The goal was to predict how humans perceive human- and AI-generated news differently based on the collected physiological data. Various data analysis techniques were used to better understand physiological responses and news perceptions. The feasibility of predicting the origin of news, whether it is human- or AI-generated, and whether it is true or fake news based on the user data was assessed. Additionally, we explored how users' general personality and behavioral traits may relate to their ability to classify the news correctly.