Rohit Gunti

I go by Rohit. Pronouced as “row hit”

Work Experience

Service & Outreach

ORCID: 0000-0002-5239-2419

Feb 19, 2024

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by

grohitraj

Hey there!

If you believe that integrating AI into existing information systems will lead to ethical or misinformation concerns, then you have all the need and urgency to research on addressing those concerns rather than dismissing AI
-RG

About the Scholar

My research interests have evolved over time due to my interdisciplinary educational background. During my bachelor’s studies, I focused on assembling, interfacing, and coding sensor-based hardware systems, as well as applying predictive analytics, anomaly detection, and visualization techniques.

These interests expanded in my master’s program to include work on corals and medical diagnostic images, exploring and evaluating open-source Information Systems (IS) available on platforms like GitHub, HuggingFace, Kaggle, and similar platforms. Many of these open-source IS systems are candidates for potential integration but require evaluation prior to implementation. I have applied AI across various contexts, such as environmental conservation, biodiversity monitoring, and human medical diagnostic support. Most of my research has centered on developing and assessing classification systems for biodiversity and health sciences. I am actively applying insights from studying how AI influences doctor-patient interactions through clinical decision support tools to contribute to the design and assessment of intelligent healthcare technologies. In my doctoral program, after reviewing literature in social computing, psychology, and libraries, my dissertation focuses not only on exploring and evaluating AI algorithms but also on examining how AI shapes and is shaped by society.

My dissertation builds on the theoretical constructs “Integrative Model of Organization Trust,” grounded in Organizational Psychology and Management Studies, and addresses ethical issues like AI’s trustworthiness, based on qualities such as fairness, robustness, reliability, accuracy, faithfulness, and transparency. Additionally, my research tackles issues like bias, data scarcity, ambiguity in method selection, and the lack of relevant evaluations. Addressing these challenges will enable organizations to potentially integrate AI into existing tools and will impact roles such as managers, customers, information professionals, instructors, students, and business/government communities, helping them use AI effectively. Hence, my proposed dissertation aims to develop a coding-free methodology for customizing Large Language Models to automate information services in libraries and similar institutions. This coding-free approach is expected to increase the adoption of LLMs for information services in archives, libraries, galleries, and museums.

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