Hilda Hadan

Hilda Hadan, Michaela Valiquette, Lennart E. Nacke et al.

Commercial Virtual Reality (VR) transforms people's virtual experiences but introduces deceptive design opportunities that threaten user privacy. Although privacy deceptive patterns on 2D platforms are well-documented, their impacts in VR remain understudied. We surveyed 481 users' experiences and responses to privacy deceptive patterns across eight commercial VR scenarios. We found that VR deceptive design can exploit both cognitive vulnerabilities and bodily strain, a phenomenon we define as Ergonomic Susceptibility, and that VR's sensory-rich experiences can make users more likely to accept invasive data disclosure framed as immersion-preserving. Users recognized manipulation but their prior non-VR exposure can foster privacy resignation. Our study shows ergonomics is a critical factor in future privacy-preserving VR design, and urges VR researchers, designers, and policymakers to develop ethical design and privacy management solutions that account for VR's unique multimodal, immersive, and ergonomic properties, building immersive experiences that respect user privacy and mitigate manipulative data practices.

Joseph Tu, Hilda Hadan, Derrick M. Wang et al.

This workshop paper presents a critical examination of the integration of Generative AI (Gen AI) into the academic writing process, focusing on the use of AI as a collaborative tool. It contrasts the performance and interaction of two AI models, Gemini and ChatGPT, through a collaborative inquiry approach where researchers engage in facilitated sessions to design prompts that elicit specific AI responses for crafting research outlines. This case study highlights the importance of prompt design, output analysis, and recognizing the AI's limitations to ensure responsible and effective AI integration in scholarly work. Preliminary findings suggest that prompt variation significantly affects output quality and reveals distinct capabilities and constraints of each model. The paper contributes to the field of Human-Computer Interaction by exploring effective prompt strategies and providing a comparative analysis of Gen AI models, ultimately aiming to enhance AI-assisted academic writing and prompt a deeper dialogue within the HCI community.

Hilda Hadan, Reza Hadi Mogavi, Leah Zhang-Kennedy et al.

The rapid growth of artificial intelligence (AI) technologies has raised major privacy and ethical concerns. However, existing AI incident taxonomies and guidelines lack grounding in real-world cases, limiting their effectiveness for prevention and mitigation. We analyzed 202 real-world AI privacy and ethical incidents to develop a taxonomy that classifies them across AI lifecycle stages and captures contributing factors, including causes, responsible entities, sources of disclosure, and impacts. Our findings reveal widespread harms from poor organizational decisions and legal non-compliance, limited corrective interventions, and rare reporting from AI developers and adopting entities. Our taxonomy offers a structured approach for systematic incident reporting and emphasizes the weaknesses of current AI governance frameworks. Our findings provide actionable guidance for policymakers and practitioners to strengthen user protections, develop targeted AI policies, enhance reporting practices, and foster responsible AI governance and innovation, especially in contexts such as social media and child protection.

Hilda Hadan, Derrick Wang, Reza Hadi Mogavi et al.

Generative AI (GenAI) use in research writing is growing fast. However, it is unclear how peer reviewers recognize or misjudge AI-augmented manuscripts. To investigate the impact of AI-augmented writing on peer reviews, we conducted a snippet-based online survey with 17 peer reviewers from top-tier HCI conferences. Our findings indicate that while AI-augmented writing improves readability, language diversity, and informativeness, it often lacks research details and reflective insights from authors. Reviewers consistently struggled to distinguish between human and AI-augmented writing but their judgements remained consistent. They noted the loss of a "human touch" and subjective expressions in AI-augmented writing. Based on our findings, we advocate for reviewer guidelines that promote impartial evaluations of submissions, regardless of any personal biases towards GenAI. The quality of the research itself should remain a priority in reviews, regardless of any preconceived notions about the tools used to create it. We emphasize that researchers must maintain their authorship and control over the writing process, even when using GenAI's assistance.