Home >industry >infotech >How India’s public policy can take maximum advantage of AI

Defined as “the science and engineering of making intelligent machines, especially intelligent computer programmes" by the late John McCarthy—one of the founding fathers of the discipline—Artificial Intelligence, or AI, has subtly made inroads into the daily lives of Indian citizens in the form of app-based cab aggregators and digital assistants on smartphones.

However, public policy in India has not been able to take much advantage of AI applications, suggests a report published jointly by the Associated Chambers of Commerce and Industry of India (Assocham) and consulting firm PwC. The report titled Artificial Intelligence and Robotics–2017 believes that national initiatives like Make in India, Skill India and Digital India could immensely benefit from AI technologies.

Alternatively, early public sector interest in AI could trigger a spurt of activity in the AI field in India.

AI, for instance, can be applied to Prime Minister Narendra Modi’s initiatives such as the Digital India initiative, Skill India and Make in India; in large-scale public endeavours ranging from crop insurance schemes, tax fraud detection, and detecting subsidy leakage, and to helping hone the country’s defence strategy.

AI, the report states, can also be consumed in traditional industries like agriculture. The department of agriculture cooperation and farmers welfare, ministry of agriculture runs the Kisan Call Centres across the country to respond to issues raised by farmers instantly and in their local language.

An AI system could help in assisting the call centre by linking available information. It could pick up soil reports from government agencies and link them to the environmental conditions prevalent over the years using data from a remote sensing satellite. The call centre could, then, provide advice on the optimal crop that can be sown in that land pocket. This information could also be used to determine the crop’s susceptibility to pests.

Necessary pre-emptive measures can then be taken—for instance, supplying the required pesticides to that land pocket as well as notifying farmers about the risk.

With a high level of connectivity, this is a feasible and ready to deploy solution which uses AI as an augmentation to the system.

An enabling infrastructure

Compared to the West and front runners of AI adoption in Asia, such as China and South Korea, the culture and infrastructure needed to develop a base for the adoption of AI in mainstream applications in India is in need of an impetus, the report acknowledges.

To begin with, Indian academics, researchers and entrepreneurs face a more acute challenge than companies in terms of the less-than-ideal infrastructure available for an AI revolution in India.

For example, cloud computing infrastructure, which is capable of storing large amounts of data and facilitating the huge amount of computing power essential for AI applications, is largely located on servers abroad. Hence, an AI-supportive cultural environment will require homegrown infrastructure.

India will also require ecosystem-fostering innovation. Fostering a culture of innovation and research beyond the organization is common to global technology giants. To encourage the same level of innovation in AI research efforts in India, initiatives to hold events and build user communities in the field of AI will go a long way, the report notes.

The main dichotomy that the regulations will have to deal with relates to who will be liable for the activities of AI systems. These systems are designed to be creative and to continue learning from the data analysed.

Hence, designers may not be able to understand how the system will work in the future. For instance, while the US is currently in the process of implementing laws concerning driverless vehicles, India still lags behind.

Instead of waiting for technology to reach a level where regulatory intervention becomes necessary, India could be a front runner by establishing a legal infrastructure in advance, the report suggests.

Issues of scale

Deep Learning, a part of AI, can be employed to tackle issues of scale often prevalent in the execution of government schemes, the PwC-Assocham report notes.

It is essentially a process that can be used for pattern recognition, image analysis and natural language processing (NLP) by modelling high-level abstractions in data which can then be compared with various other recognized contents in a conceptual way rather than using just a rule-based method.

The report cites the example of the Clean India initiative, directed towards the construction of toilets in rural India.

Public servants are tasked with uploading images of these toilet constructions to a central server for sampling and assessment. Image processing AI, the PwC-Assocham report suggests, can be used to flag photographs that do not resemble completely built toilets.

Image recognition capabilities can also be used to identify whether the same official appears in multiple images or if photos have been uploaded by officials from a location other than the intended site.

Considering the scale of this initiative, which involves creating more functional toilets, being able to check every image rather than a small sample will actually help increase effectiveness.

Ethical, legal and social implications

Last but not the least, to reap the societal benefits of AI systems, we would need to be able to trust them and ensure that they comply with an ethical, moral and social framework analogous to that for humans, notes the PwC-Assocham report.

It urges that research efforts must be concentrated on implementing regulations in AI system design that are updated on a continual basis to respond appropriately to different application fields and actual situations.

The design philosophy must be such that it ensures security against external attacks, anomalies and cyberattacks, the PwC-Assochamreport insists, adding that policy initiatives should explicitly touch upon building an incubatory environment for AI-based research and training.

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