Visualizing Local Information Inequality in South Korea: An AI-Based Approach Using Public Library Data

The goal of the project is to understand information inequality across geographical regions in South Korea and visualize them using an AI-backed visualization tool. Our plan, spanning three years, revolves around the development of an intuitive platform for the purpose of visualizing these disparities. During the first year, we aim to construct comprehensive metrics for assessing the level of informational inequality, based on the theory of local information landscapes (LIL theory). The subsequent year will be dedicated to the development of a predictive model leveraging artificial intelligence, designed to forecast future trends of inequality. This model will be constructed based on features extracted from survey and open library data. By the final year, our aim is to develop a fully-functioning, user-friendly prototype of an AI-powered platform. This research combines expertise from information science, data science, and information technology. Also, we employ a multifaceted research methodology, incorporating both qualitative and quantitative strategies. The aim extends beyond merely propelling scientific comprehension such as contextualizing LIL theory in the health and education domains; we also aspire to utilize our findings to guide policy-making, thereby augmenting its objectivity and grounding in empirical studies. We endeavor to make this information universally accessible, which we believe will elevate transparency and fortify accountability in decision-making processes. Additionally, the execution and results of this project have the potential to invigorate the data, AI, and platform sectors. One significant outcome we anticipate is enhancing data and AI literacy among local residents and other stakeholders, thereby laying a solid foundation for the development of future educational initiatives. 

* This research is funded by the National Research Foundation of Korea (NRF), an NSF-equivalent in South Korea (Award #2023S1A5A2A21087977).

* PI: Jongwook Lee (Kyungbuk National University iSchool).
* Co-PIs: Myeong Lee (GMU), Sanghee Oh (SKKU), Seungwon Yang (LSU), and Kwonho Choi (KNU). 

* Project Period: June 2023 - May 2026

* Funding: ₩565,803,000 = $427K