Day 54 – Q 3. How can machine learning and artificial intelligence help in good governance? Explain with the help of suitable examples.

3. How can machine learning and artificial intelligence help in good governance? Explain with the help of suitable examples. 

मशीनी शिक्षा और कृत्रिम बुद्धिमत्ता सुशासन में कैसे मदद कर सकती है? उपयुक्त उदाहरणों की सहायता से समझाएँ।

Introduction:

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. And, Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves. 

Body

Applications of machine learning and artificial intelligence in good governance:

• Agriculture being a dominant occupation, there is huge potential to deal with crop failure and other agricultural issues using machine learning.

Image recognition and deep learning models have enabled distributed soil health monitoring without the need of laboratory testing infrastructure. AI solutions integrated with data signals from remote satellites, as well as local image capture in the farm, have made it possible for farmers to take immediate actions to restore soil health.

• India being one of the largest consumers of telecommunication services, machine learning can be used in various socio-economic causes via telecommunication.
• Border management with hostile neighbourhood can be done through computer vision, including object recognition, etc. with minimal loss of life and property.

• Crime and investigations, huge pending cases are serious problem before Indian judiciary and administration. Machine learning can help in solving cases, bioinformatics and DNA profiling etc. 

• Artificial Intelligence can expedite achievement of the SDGs. For example Population Foundation of India is carrying out a project in North India using AI to give adolescents access to sexual and reproductive health information.
• The Government of India has been making a series of large scale interventions to address India’s
• Healthcare challenges, viz. transformation of 1.5 lakh Health and Wellness Centers, developing district hospitals to cater to long-term care for non-communicable diseases, Ayushman Bharat Mission, promoting e-Health etc. AI combined with robotics and Internet of Medical Things (IoMT) could potentially be the new nervous system for healthcare, presenting solutions to address healthcare problems. 

For example – Integrating AI capabilities to this device using Microsoft’s retinal imaging APIs enables operators of 3Nethra device to get AI-powered insights even when they are working at eye checkup camps in remote areas with nil or intermittent connectivity to the cloud.

• Predictive tools to inform pre-emptive action for students predicted to drop out of school – For instance, in a recent preliminary experiment conducted in Andhra Pradesh, AI applications processed data on all students based on parameters such as gender, socioeconomic factors, academic performance, school infrastructure, teacher skills, etc., with the objective of helping the government identify students likely to drop out. Test results could inform suggestions to enroll students in vocational studies. Additionally, redressal mechanisms could be put in place to identify students whose performance can be improved by focus of existing schemes to their family.
• Cyber-attacks seem to pose a great threat to our institutions and public systems, today. AI technologies possess the capability to detect vulnerabilities and take remedial measures to minimise exposure of secure online platforms containing highly sensitive data from being targeted by unscrupulous social elements.
• Through the use of an intelligent traffic management system including sensors, CCTV cameras, automatic number plate recognition cameras, speed detection cameras, signalised pedestrian crossings and stop line violation detection systems and the use of AI, real time dynamic decisions on traffic flows such as lane monitoring, access to exits, toll pricing, allocating right of way to public transport vehicles, enforcing traffic regulations through smart ticketing etc. can be made.

Conclusion

India’s unique challenges and aspirations, combined with the advancement in AI, and a desire to assume leadership in this nascent technology means India’s approach towards AI strategy has to be balanced for both local needs and greater good. The way forward for India in AI has to factor in our current strengths in AI, or a lack there of, and thus requires large scale transformational interventions, primarily led by the government, with private sector providing able support.