Predicting Poverty and its Alleviation using AI

Introduction

This Short paper presents a perspective on application of AI innovations in real time prediction and alleviation of poverty. I woke up to a news item that was distressful not because the American Port workers are still on strike but are fighting against the elevated use of Automation which is causing the job losses. It was depressing to see that 'Automation as part of problem statement' at ports in failed collective bargaining negotiations between the International Longshoremen's Association and the U.S. Maritime Alliance failed(Atkinson, 2024).

It made me relook at Generative AI and bring a different paradigm approach to it. I take an example of large country and diverse society like India where the digital reach has increased significantly in last decade (NPCL, 2022). I introspect on real time policy design, bringing dynamic improvements, having practical and effective implementation using GenAI as solution. Poverty Eradication has seen through many troughs and peaks in hibernation modes and political posturing yet remains an elusive dream (JNU, 2022).

Problem Statement

India is at the forefront of fighting poverty successfully, yet its speed is limited due to basic discrepancies in auto correction of its policy design of pre-2010 (Alagh, 2010). The Biggest Problem Poverty alleviation policies and their implementation face today are linked to their dynamic intelligence deficit and their real time effective measurement metrics. Non-AI measurement methods cause decision days and policy gap in poverty alleviation programs. Such mis opportunities lead to public expenditure wastage and hence making programs ineffective. This Paper delves into some examples use cases that can help overcome policy design deficiencies and become catalyst in fast implementation of the poverty alleviation programs by design a real time integrated AI platform.

Literature Review of Current Solution or traditional methods

Policy design of India is historically based upon the national survey commission and reactive analysis to define Below Poverty Line (BPL) framework since 1979 based on nutritional requirements (Raveendran, 2010). Although Tendulkar Framework of 2009 and Rangarajan Report upgraded policy to income groups etc, but they weren’t resilient and lacked certainty. Hence Niti Ayog (Niti Ayog and GOI, 2014) adapted to accommodate the new concept of UN adopted Multidimensional poverty measurement and Analysis Index MPI (Alkire et al., 2015). The dual-cutoff approach of the Alkire-Foster (AF) methodology captures broad qualitative aspects of people’s life across 3 dimensions – Health, Education, and Standard of living. India retained 10 indicators from the Global MPI and added 2 new ones namely Maternal Health and Bank Account. Yet majority of programs and policies are based on aggregated NSSO survey reports and commission rather than full real time insights (Suman Satapathy et al., 2018).

Adaption, Adoption and Evolution of AI Platform Design

A detailed research paper in Tashkent University maps the non-monetary and monetary AI techniques for poverty alleviation, using unstructured data classification of field data and labelled remote sensing to Predict Poverty (Usmanova et al., 2022). Even Prediction of Climatic conditions impacting future poverty index interlinked with multi-dimensional features is well defined in AI models today (Ayoo, 2022). Hence a three stage Adapt, Adopt and Evolve perspective derives out

Adaption

AI Framework of Machine Learning, Deep learning and GenAI are seen to have huge potential to achieve UN laid Sustainability Development goals (Singh et al., 2024). The context of scale in India is enormous, with disproportionate complexity, sharp disparities and regional diversity, hence constant refinement of any policy or model through AI modelling addresses the dynamic changes in macro-Economic political Environment (Tiwari, 2021).The wider adaption of AI is fundamentally based on data availability , quality and effective insights utilised, hence using less existing non-intrusive means is key to data gathering especially in rural and low income areas (Ascher, 2021)

Adoption

Few use cases for adoption are high-resolution satellite imagery combined with machine learning to predict poverty levels in African countries (Bennington-Castro, 2017). AI-driven precision farming technologies optimize?(Kumar et al., 2024) for example Tanzania (Hillary and Scott-Briggs, 2024). AI-powered Financial Inclusion of poor and marginalised in India (Bhawnra and Singh, 2024). Optimised or free Healthcare Delivery through connected care and AI led predictive Care(Kriwet, 2020).??

Evolution of Design

?The Design can also work as per INDIA AI MISSION launched for catalysing AI Innovation, Investment and development of sovereign Indigenous Foundational Models (GOI, 2024).Key USP of this platform is the aggregated modern AI ways to adapt, adopt and evolve over already available systems.

Effectiveness of Measured Benefits and Challenges

Effective Objective Benefits Management

Benefits can be achieved significantly utilizing AI for Poverty alleviation by measuring business outcomes too

1.????? No of Targeted Interventions YOY 30% Uptick after vast analysis of data, identify the most vulnerable pockets for more precise targeting.

2.????? Predictive Analytics ROC|AUC|Model accuracy Score predicting future poverty trends to anticipate & mitigate potential crises. ?

3.????? Resource Allocation EOQ | SSQ | Lead time analysis optimize allocation of resources via efficient distribution of aid and services. ?

4.????? Monitoring and Evaluation Response & Resolution Experience level Agreement: AI models can continuously monitor real-time data for the effectiveness of policies to make needed corrections and ensure automated feedback loop.

5.????? Data-Driven Decision Making YOY 30% Uptick with recall ratio insights based on Supervised or unsupervised learning and make informed decision without intuition or bias.

6.????? Identifying Root Causes YOY 30% Uptick: uncover the underlying causes of poverty by deep learning in complex datasets.

7.????? Community Engagement XLA| NPP: AI tools can facilitate better communication and engagement with communities.

8.????? Scenario Planning YOY 30% Uptick: simulate different policy scenarios and their potential impacts to evaluate the effectiveness before implementation.

AI for Social Good is the International Research Centre on Artificial Intelligence (IRCAI) under UNESCO and has developed such effective frameworks to evaluate AI’s impact on poverty alleviation. Likewise, this Platform in INDIA would bring Precision Poverty Alleviation, leveraging existing big data and AI investments to identify and support impoverished households, bring Realtime AI-powered platforms for Jan Dhan accounts or connect ground level NGOs ensuring effective resourcefulness. ??

Challenges to Overcome

Many challenges need to be addressed for this integrated platform for example Data Quality and Availability, Technical Expertise, partnerships with institutions. Expensive Infrastructure Limitations, Ethical and Privacy Concerns about data usage. Bias and Fairness in Fundamental models, Regulatory and Legal Frameworks with Public Trust and Acceptance through interdisciplinary Collaboration and fostering interdisciplinary partnerships can be challenging.

Bibliography

Alagh, Y.K. (2010) The Poverty Debate in Perspective: Moving Forward with the Tendulkar Committee. Indian Journal of Human Development. 4(1), 33–44.

Alkire, S., Roche, J.M., Ballon, P., Foster, J., Santos, M.E., Seth, S. (2015) Multidimensional poverty measurement and analysis. Oxford University Press, USA.

Ascher, W. (2021) Coping with intelligence deficits in poverty-alleviation policies in low-income countries. Policy Sciences. 54(2), 345–370.

Atkinson, H. (2024) Strike at U.S. ports brings debate over automation front and center.

Ayoo, C. (2022) Poverty Reduction Strategies in Developing Countries. In Rural Development - Education, Sustainability, Multifunctionality. IntechOpen.

Bennington-Castro, J. (2017) AI Is a Game-Changer in the Fight against Hunger and Poverty. Here’s Why.

Bhawnra, S.X., Singh, K.B. (2024) Artificial Intelligence for Financial Inclusion in India. In Conversational Artificial Intelligence. pp. 589–606.

GOI, P.M.O. (2024) Cabinet approves ambitious IndiaAI mission to strengthen the AI innovation ecosystem.

Hillary, Scott-Briggs, A. (2024) Innovative solutions for social change: The role of artificial intelligence in poverty alleviation.

JNU, B.H.D. (2022) Poverty eradication India’s biggest challenge.

Kriwet, C. (2020) Here are 3 ways AI will change healthcare by 2030. World Economic Forum.

Kumar, R., Farooq, M., Qureshi, M. (2024) Chapter 10 - Advancing precision agriculture through artificial intelligence: Exploring the future of cultivation. In A. Hamadani, N. A. Ganai, H. Hamadani, & J. Bashir, eds. A Biologist?s Guide to Artificial Intelligence. Academic Press, pp. 151–165.

Niti Ayog, GOI (2014) REPORT OF THE EXPERT GROUP TO REVIEW THE METHODOLOGY FOR MEASUREMENT OF POVERTY Government of India Planning Commission.

NPCL (2022) Digital Payments Adoption in India, 2020.

Raveendran, G. (2010) New Estimates of Poverty in India: A Critique of the Tendulkar Committee Report. Indian Journal of Human Development. 4(1), 75–89.

Singh, R., Kumar, K., Khan, S. (2024) Chapter 12 A Comprehensive View of Artificial Intelligence (AI)–Based Technologies for Sustainable Development Goals (SDGs). In R. Singh, S. Khan, A. Kumar, & V. Kumar, eds. An Emerging Economy Perspective. De Gruyter, pp. 183–196.

Suman Satapathy, S., Satapathy, S.S., Jaiswal, K.K. (2018) A Study on Poverty Estimation and Current State of Poverty in India.

Tiwari, R. (2021) International Journal of Social Science and Economic Research HOW ARTIFICIAL INTELLIGENCE (AI) CAN HELP IN POVERTY REDUCTION IN INDIA.

Usmanova, A., Aziz, A., Rakhmonov, D., Osamy, W. (2022) Utilities of Artificial Intelligence in Poverty Prediction: A Review. Sustainability (Switzerland). 14(21).

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