{"type":"ScholarlyArticle","title":"Learning and Reasoning Multifaceted and Longitudinal Data for Poverty Estimates and Livelihood Capabilities of Lagged Regions in Rural India","authors":[{"name":"Atharva Kulkarni","slug":"atharva-kulkarni","given":"Atharva","family":"Kulkarni","affiliation":"Language Technologies Institute , Carnegie Mellon University , USA"},{"name":"Raya Das","slug":"raya-das","given":"Raya","family":"Das","affiliation":"Indian Council for Research on International Economic Relations , New Delhi , India"},{"name":"Ravi S. Srivastava","slug":"ravi-s-srivastava","given":"Ravi S.","family":"Srivastava","affiliation":"Centre for Employment Studies , Institute for Human Development , Delhi , India"},{"name":"Tanmoy Chakraborty","slug":"tanmoy-chakraborty","given":"Tanmoy","family":"Chakraborty","affiliation":"Indian Institute of Technology Delhi , India"}],"abstract":"Poverty is a multifaceted phenomenon linked to the lack of capabilities of households to earn a sustainable livelihood, increasingly being assessed using multidimensional indicators. Its spatial pattern depends on social, economic, political, and regional variables. Artificial intelligence has shown immense scope in analyzing the complexities and nuances of poverty. The proposed project aims to examine the poverty situation of rural India for the period of 1990-2022 based on the quality of life and livelihood indicators. The districts will be classified into ‘advanced’, ‘catching up’, ‘falling behind’, and ‘lagged’ regions. The project proposes to integrate multiple data sources, including conventional national-level large sample household surveys, census surveys, and proxy variables like daytime, and nighttime data from satellite images, and communication networks, to name a few, to provide a comprehensive view of poverty at the district level. The project also intends to examine causation and longitudinal analysis to examine the reasons for poverty. Poverty and inequality could be widening in developing countries due to demographic and growth-agglomerating policies. Therefore, targeting the lagging regions and the vulnerable population is essential to eradicate poverty and improve the quality of life to achieve the goal of ‘zero poverty’. Thus, the study also focuses on the districts with a higher share of the marginal section of the population compared to the national average to trace the performance of development indicators and their association with poverty in these regions.","doi":"10.24963/ijcai.2023/709","url":"https://ijcai.org/proceedings/2023/709","pdf":"https://ijcai.org/proceedings/2023/0709.pdf","markdown":"https://ijcai.org/proceedings/2023/709.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/709","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good - Projects","track":"AI for Good - Projects","number":709,"pages":{"first":6386,"last":6394},"keywords":["AI for Good - Projects: Humans and AI","AI for Good - Projects: Data Mining","AI for Good - Projects: Multidisciplinary Topics and Applications"],"videos":[],"references":[{"position":1,"raw":"[Addison and Stewart, 2015] Douglas M Addison and Ben- jamin Stewart. 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