Indian Institute of Technology (IIT), Kanpur today signed a memorandum of understanding (MoU) with the Ministry of Defence (MoD) for developing a prompt and predictive grievance redress mechanism using artificial intelligence (AI) and machine learning tools.
A tripartite MoU was signed between IIT Kanpur, MoD (Department of Defence) and Department of Administrative Reforms & Public Grievances (DAR&PG) in the presence of Defence minister Rajnath Singh, union minister of state for Personnel Jitendra Singh and IIT Kanpur Deputy Director S Ganesh.
The partnership pertains to conducting and developing exploratory and predictive analysis of public grievances received in the portal of Centralised Public Grievance Redress and Monitoring System (CPGRAMS) using AI, machine learning and statistics.
Earlier, IIT Kanpur had signed a MoU with defence public sector undertaking (PSU) Bharat Dynamics Limited (BDL) for manufacturing affordable ventilators to treat covid-19 patients.
“Our project for developing an affordable ventilator has been going from strength to strength and now with Bharat Dynamics supporting us, we will be able to scale up production and make this device widely available as a ‘Make in India’ product,” IIT Kanpur director Abhay Karandikar had then said.
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Meanwhile, the project ‘Analysing Grievances Data using Artificial Intelligence’ is being collaborated with the faculties of IIT Kanpur viz. Prof Shalabh, Dept of Mathematics and Statistics, Prof Piyush Rai and Prof Nisheeth Srivastava from the Dept of Computer Science and Engineering.
According to the IIT Kanpur, the analysis will help the MoD to assess if more complaints were coming from any specific areas/regions/cities etc. The developed dashboard will help the concerned official to take corrective measures in time, preferably before the problem becomes large.
Such models will help the ministry in taking preventive steps by timely interventions so that the number of grievances is reduced over a period of time.
The MoD and DARPG receive grievances from people from all parts of the country which are of different nature. These grievances are addressed by corresponding divisions of various ministries.
Meanwhile, 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/severity/sentiment of the grievances.
As soon as the complaint comes, it is planned that it gets automatically directed to the concerned official, where it is resolved and the feedback is obtained automatically without requiring human intervention. Besides, another aim is to predict the quality of redress response by the official. The analysis and modelling of data will help the MoD in bringing about systematic changes and policy interventions.
It is expected the new model would facilitate responses to the people by the ministries become easier, swifter, faster and more efficient. The ministries can also make systematic changes in the policy designs.