Karthik Bhat

Most personal wellbeing apps present summative dashboards of health and physical activity metrics, yet many users struggle to translate this information into meaningful understanding. These apps commonly support engagement through goals, reminders, and structured targets, which can reinforce comparison, judgment, and performance anxiety. To explore a complementary approach that prioritizes self-reflection, we design KRIYA, an AI wellbeing companion that supports co-interpretive engagement with personal wellbeing data. KRIYA aims to collaborate with users to explore questions, explanations, and future scenarios through features such as Comfort Zone, Detective Mode, and What-If Planning. We conducted semi-structured interviews with 18 college students interacting with a KRIYA prototype using hypothetical data. Our findings show that through KRIYA interaction, users framed engaging with wellbeing data as interpretation rather than performance, experienced reflection as supportive or pressuring depending on emotional framing, and developed trust through transparency. We discuss design implications for AI companions that support curiosity, self-compassion, and reflective sensemaking of personal health data.

Screen use pervades daily life, shaping work, leisure, and social connections while raising concerns for digital wellbeing. Yet, reducing screen time alone risks oversimplifying technology's role and neglecting its potential for meaningful engagement. We posit self-awareness -- reflecting on one's digital behavior -- as a critical pathway to digital wellbeing. We developed WellScreen, a lightweight probe that scaffolds daily reflection by asking people to estimate and report smartphone use. In a two-week deployment (N=25), we examined how discrepancies between estimated and actual usage shaped digital awareness and wellbeing. Participants often underestimated productivity and social media while overestimating entertainment app use. They showed a 10% improvement in positive affect, rating WellScreen as moderately useful. Interviews revealed that structured reflection supported recognition of patterns, adjustment of expectations, and more intentional engagement with technology. Our findings highlight the promise of lightweight reflective interventions for supporting self-awareness and intentional digital engagement, offering implications for designing digital wellbeing tools.

Understanding the limitations and weaknesses of state-of-the-art models in artificial intelligence is crucial for their improvement and responsible application. In this research, we focus on CLIP, a model renowned for its integration of vision and language processing. Our objective is to uncover recurring problems and blind spots in CLIP's image comprehension. By delving into both the commonalities and disparities between CLIP and human image understanding, we augment our comprehension of these models' capabilities. Through our analysis, we reveal significant discrepancies in CLIP's interpretation of images compared to human perception, shedding light on areas requiring improvement. Our methodologies, the Discrepancy Analysis Framework (DAF) and the Transformative Caption Analysis for CLIP (TCAC), enable a comprehensive evaluation of CLIP's performance. We identify 14 systemic faults, including Action vs. Stillness confusion, Failure to identify the direction of movement or positioning of objects in the image, Hallucination of Water-like Features, Misattribution of Geographic Context, among others. By addressing these limitations, we lay the groundwork for the development of more accurate and nuanced image embedding models, contributing to advancements in artificial intelligence.