Designing Intelligent Automation based Solutions for Complex Social Problems
This work addresses complex social problems in low-income geographies, but it is incremental as it applies existing methods to a new domain without significant methodological innovation.
The paper tackles the challenge of designing effective preventive measures for complex social problems in low-income regions by proposing a data-driven machine learning framework, illustrated using survey data from adolescent girls in India.
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.