National Strategy On Artificial Intelligence: NITI Aayog

The budget speech of 2018-19 was marked by mandating the planning body of Government of India(GOI) NITI Aayog to establish the national level program on Artificial intelligence (henceforth AI), taking into account the emerging technologies. In carrying out the stated objectives, NITI Aayog has taken up a multipronged approach by undertaking exploratory proof-of-AI projects in diverse areas, working out a national strategy for the dynamic AI ecosystem in India, and collaborating with experts and stakeholders. (NITI Aayog, 2020). Leading players from the industry tied up in the following year to implement AI projects in critical areas like healthcare and agriculture. Later in June 2018 after having consultations from industry experts and stakeholders, the think tank came up with a discussion paper that lays the road map of government’s vision for introducing AI-powered technologies for application into five key areas i.e. healthcare, education, agriculture, urban infrastructure transportation respectively.

The paper has been presented as an attempt to ensure social and inclusive growth in line with the development philosophy of the government. It provides an overview of evolving AI technologies, global developments with the best country practices as the benchmark, and then highlighting priority areas that offer the opportunity to maximize the greater good. Also, given the historical contribution of the IT sector to the Indian economy, it aims at providing solutions to countries across the globe. The need for giving thrust to AI technology is supported by the potential contribution that AI offers to national GDP.

While benchmarking the countries for best practices, the report focuses mainly on China, U.K, France, Japan but the recent government Artificial Intelligence Readiness Index 2019(*) (Table1.1) shows that only the U.K. ranks among the top five nations. There was a scope to consider best practices from European nations and others which rank higher.

Government AI Readiness Index 2020

In 2019 India ranked 17 among 194 countries which have not been discussed in detail. The paper highlights the McKinsey Global Institute AI adoption and use survey showing the shift in Current AI adoption and future AI investments by sector where Financial services, High tech telecommunications, and Automotive and assembly are the leaders. The thinktank lays emphasis on seeing AI technology as a means to improve efficiency in existing processes and not overhauling traditional tasks. “AI+X” implies the deployment of AI on existing processes(X) to make value addition and improve productivity.

While explaining the key enablers and potential areas of application, the paper points to key sectors where AI solutions offer faster and smarter implementation of assigned tasks and accrue welfare gains. In healthcare, given the shortage of trained medical practitioners, AI offers enhanced monitoring tools that can help in early detection of diseases like cancer using machine learning techniques and hence reducing the cost of care.

For the agriculture sector, the paper pointed out the key benefits of precision agriculture using AI-driven drones, soil sensors, autonomous tractors, etc. there have been various notable initiatives. One notable contribution has been from Microsoft and ICRISAT which developed an app that advises farmers on sowing, fertilizer application which has led to an increase in Kharif yield in Karnataka and AP.

While In the education sector, an intelligent tutoring system with personalized feedback without human intervention was proposed, for Smart cities and infrastructure AI can help in improving security, efficient lighting, and energy conservation for cities. In terms of smart mobility and transportation, AI offers improved traffic management systems, crowd control, and use of autonomous transportation particularly.

However, for Artificial intelligence to thrive in India, many challenges need to be addressed. Given the commonality of problems among different sectors, the paper calls for an integrated approach especially stemming from a lack of reliable data for efficient use of AI across different sectors. Privacy concerns like Facebook- Cambridge Analytica's data misuse call for the need to develop skilled professionals. Besides this ethical dimensions cannot be sidelined, even the right to privacy must be effectively enforced.

To promote AI research in India, the paper presented a four-tier framework (**). The paper acknowledges the ethical implications of AI and talks about explainable AI i.e. XAI and privacy issues which cannot be ignored under the section notion of responsible AI and explainable AI(***). The paper gives no projection or estimates for the amount required for establishing AI supporting infrastructure. The feasibility of proposed initiatives remains a testable proposition given mixed results from different sectors like; an impact assessment of the 175 farmers in the pilot group reflected a 30% increase in their crop yield per hectare.(Dharmaraj and Vijayanand, 2018). A study by Zawacki-Richter et al. (2019) on AI in education(AIEd) points out the lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education. Popenici, S., & Kerr, S. (2017) calls for research on ethical implications of current control on AI development and resource richness of human knowledge with few monopoly bodies.

The government of India mandated the previous two budgets presented by the union government of India highlight the enhanced focus on developing research ecosystems to address skilling challenges and promote adoption in globally valued skills like Artificial Intelligence, language training, IoT, Big Data, and Robotics(GOI,2019). In the recent budget (GOI,2020), with a special focus on Wellness, water, and Sanitation the government has laid importance on designing preventive regimes using machine learning and AI to target diseases(Union Budget, 2020). The national policy on official statistics aims at promoting the latest technologies including AI and lay down the roadmap for modernized data collection, integrated information portal, and ensure timely dissemination of information. Modern AI systems driven by machine learning algorithms are like black boxes and pose an immediate threat to intent and causation tests that appear legally in every domain. Merely imposing strict liabilities or defining transparency standards at a granular level for AI design and use will not serve the purpose. In the process, small firms face entry barriers. Rather, the sliding scale system as highlighted by Yavar Bathaee( 2018) offers a better way out.

Notes:

*1 In 2017, Oxford Insights created the world’s first Government AI Readiness Index, to answer the question: how well placed are national governments to take advantage of the benefits of AI in their operations and delivery of public services? The results sought to capture the current capacity of governments to exploit the innovative potential of AI. The 2019 Government AI Readiness Index, produced with the support of the International Development Research Centre (IDRC), sees the development of our methodology, and an expansion of scope to cover all UN countries (from our previous group of OECD members). It scores the governments of 194 countries and territories according to their preparedness to use AI in the delivery of public services. (Oxford Insights and the International Development Research Centre, 2020)

** ICON (International Centers of New Knowledge): focusing on the creation of new knowledge through basic research, b) CROSS (Centre for Research On Sub-Systems): focusing on developing and integrating core technologies developed at ICON layer and any other sources c) CASTLE (Center for Advanced Studies, Translational Research, and Leadership): focusing on development and deployment of application-based research and d) CETIT (Centre of Excellence in Technology Innovation and Transfer): focusing on the commercialization of technologies developed

***The Explainable AI (XAI) program aims to create a suite of machine learning techniques that:

• Produce more explainable models while maintaining a high level of learning performance; and

• Enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners. (NITI Aayog,2018)

References

Dharmaraj, V., and Vijayanand, C. 2018. Artificial Intelligence (AI) in Agriculture. Int.J.Curr.Microbiol.App.Sci. 7(12): 2122-2128.

Government of India(GOI), 2019, Union Budget, India, Ministry of Finance Government of India(GOI), 2020, Union Budget, India, Ministry of Finance

Mckinsey.com. (2020). [online] Available at: https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/Our%20 Insights/How%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20c ompanies/MGI-Artificial-Intelligence-Discussion-paper.ashx [Accessed 08 Feb. 2020].

NITI.gov.in. (2020). National Strategy on Artificial Intelligence | NITI Aayog. [online] Available at: https://niti.gov.in/national-strategy-artificial-intelligence [Accessed 6 Feb. 2020].

Oxford Insights. (2020). Government AI Readiness Index 2019 — Oxford Insights — Oxford Insights. [online] Available at: https://www.oxfordinsights.com/ai-readiness2019 [Accessed 06 Feb. 2020].

Popenici, S., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and practice in technology-enhanced learning, 12(1), 22.

Yavar Bathaee( 2018) The Artificial intelligence Black Box and the Failure of Intent and Causation, Harvard Journal of Law & Technology Volume 31, Number 2 Spring 2018.

Zawacki-Richter, O., Marín, V.I., Bond, M. et al. Systematic review of research on artificial intelligence applications in higher education – where are the educators?. Int J Educ Technol High Educ 16, 39 (2019).