IIT Kanpur, Ministry of Defence to work on ‘Predictive Analysis Of Public Grievances’

The current project aims at developing the predictive models which can analyze the cause of grievances, provide insights into the nature of grievances, infer any Spatio-temporal trends in the data and predict the quality or severity or sentiment of the grievances.

A Tripartite Memorandum of Understanding (MoU) has been signed by Indian Institute of Technology (IIT), Kanpur, Ministry of Defence and Department of Administrative Reforms and Public Grievances (DARPG), Government of India. The MoU was signed in the presence of Defence Minister of India Rajnath Singh, Minister of State for Personnel Dr Jitendra Singh and Deputy Director, IIT Kanpur Professor S Ganesh. The agreement has been signed to conduct and develop the exploratory and predictive analysis of Public Grievances received in the portal of Centralized Public Grievance Redress and Monitoring System (CPGRAMS) using the tools of Artificial Intelligence (AI), Machine Learning (ML) and statistics.

The project “Analyzing Grievances Data using Artificial Intelligence” is being collaborated with the faculty of IIT Kanpur, Prof Shalabh, Department of Mathematics and Statistics, Prof Piyush Rai and Prof Nisheeth Srivastava from the Department of Computer Science and Engineering.

The Ministry of Defence and DARPG on a daily basis receive grievances from people from across the country. The grievances received are of different nature and are addressed by corresponding divisions of various ministries in the government. The current project aims at developing the predictive models which can analyze the cause of grievances, provide insights into the nature of grievances, infer any Spatio-temporal trends in the data and predict the quality or severity or sentiment of the grievances. As soon as the complaint is reviewed, it will be automatically going to the concerned officer or department. Once it is solved and feedback is obtained automatically without any requirement of human intervention.

Another aim is to predict the quality of redressal response by the serving officer. The analysis and modelling of data will help the ministry in bringing about systematic changes and policy interventions. The analysis will help the Ministry of Defence to assess if more complaints are coming from any specific areas or regions or cities etc.

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