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Tejaswi Polimetla

Publications and source records attributed to Tejaswi Polimetla.

2 recordsLinked to original sources

A Paradigm for Creative Ownership

As generative AI tools become embedded in creative practice, questions of ownership in co-creative contexts are pressing. Yet studies of human-AI collaboration often invoke "ownership" without definition: sometimes conflating it with other concepts, and other times leaving interpretation to participants. This inconsistency makes findings difficult to compare across or even within studies. We introduce a framework of creative ownership comprising three dimensions - Person, Process, and System - each with three subdimensions, offering a shared language for both system design and HCI research. In semi-structured interviews with 21 creative professionals, we found that participants' initial references to ownership (e.g., embodiment, control, concept) were fully encompassed by the framework, demonstrating its coverage. Once introduced, however, they also articulated and prioritized the remaining subdimensions, underscoring how the framework expands reflection and enables richer insights. Our contributions include 1) the framework, 2) a web-based visualization tool, and 3) empirical findings on its utility.

cs.HC

How do transportation professionals perceive the impacts of AI applications in transportation? A latent class cluster analysis

Recent years have witnessed an increasing number of artificial intelligence (AI) applications in transportation. As a new and emerging technology, AI's potential to advance transportation goals and the full extent of its impacts on the transportation sector is not yet well understood. As the transportation community explores these topics, it is critical to understand how transportation professionals, the driving force behind AI Transportation applications, perceive AI's potential efficiency and equity impacts. Toward this goal, we surveyed transportation professionals in the United States and collected a total of 354 responses. Based on the survey responses, we conducted both descriptive analysis and latent class cluster analysis (LCCA). The former provides an overview of prevalent attitudes among transportation professionals, while the latter allows the identification of distinct segments based on their latent attitudes toward AI. We find widespread optimism regarding AI's potential to improve many aspects of transportation (e.g., efficiency, cost reduction, and traveler experience); however, responses are mixed regarding AI's potential to advance equity. Moreover, many respondents are concerned that AI ethics are not well understood in the transportation community and that AI use in transportation could exaggerate existing inequalities. Through LCCA, we have identified four latent segments: AI Neutral, AI Optimist, AI Pessimist, and AI Skeptic. The latent class membership is significantly associated with respondents' age, education level, and AI knowledge level. Overall, the study results shed light on the extent to which the transportation community as a whole is ready to leverage AI systems to transform current practices and inform targeted education to improve the understanding of AI among transportation professionals.

stat.AP