Designing Intelligent Automation based Solutions for Complex Social Problems

TL;DR

This paper proposes a data-driven, machine learning-based framework for designing intelligent automation solutions to address complex social problems, demonstrated through a case study on adolescent girls in a socio-economically backward Indian region.

Contribution

It introduces a novel design framework for adaptive, low-cost automation solutions to social issues, emphasizing data analysis and machine learning in challenging contexts.

Findings

Framework enables effective preventive measures

Demonstrated with survey data from India

Supports scalable social interventions

Abstract

Deciding effective and timely preventive measures against complex social problems affecting relatively low income geographies is a difficult challenge. There is a strong need to adopt intelligent automation based solutions with low cost imprints to tackle these problems at larger scales. Starting with the hypothesis that analytical modelling and analysis of social phenomena with high accuracy is in general inherently hard, in this paper we propose design framework to enable data-driven machine learning based adaptive solution approach towards enabling more effective preventive measures. We use survey data collected from a socio-economically backward region of India about adolescent girls to illustrate the design approach.