Recommendations of the NITI Aayog's National Strategy for AI :
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 the greater good. The way forward for India in AI has to factor in our current strengths in AI, or a lack thereof, and thus requires large scale transformational interventions, primarily led by the government, with private sector providing able support.
The set of recommendation to address the biggest challenges and opportunities for India in the field of AI
Analysis of focus sectors in NITI AAYOG's National strategy for AI leads us to the assertion that the efforts need to be concentrated across major themes of research, data democratisation, accelerating adoption and re-skilling – with privacy, security, ethics and intellectual property rights permeating as common denominators for all our recommended initiatives.
These challenges, while by no means exhaustive, if addressed in an expeditious manner through concerted collaborative efforts by relevant stakeholders, with the government playing a catalytic role, could lead to fundamental building blocks that can form the core to India’s march towards achieving its goal of #AIforAll.
The first set of recommendations focus on turbocharging both core and applied research. In addition, two frameworks for solving some of AI’s biggest research challenges through collaborative, market-oriented approach have been proposed.
The new age of AI and related frontier technologies would disrupt the nature of jobs of tomorrow and the skills required to realise the true potential of these transformative technologies.
Thus, re-skilling of the existing workforce and preparing students for developing applied set of skills for the changing world of technology.
Among the several impediments towards large scale adoption of AI in India, the primary ones include difficulty in access to data (more specifically, structured and intelligent data), high cost and low availability of computing infrastructure, lack of collaborative approach to solving for AI combined with low awareness.
To address these challenges include developing large foundational annotated data sets to democratize data and multi-stakeholder marketplaces across the AI value chain (data, annotated data and AI models).
One of the key aspects of our ambition of #AIforAll includes responsible AI: ensuring adequate privacy, security and IP related concerns and balancing ethical considerations with the need for innovation.
For example, establishing a data protection framework with legal backing, establish sectoral regulatory frameworks, benchmark national data protection and privacy laws with international standards encourage AI developers to adhere to international standards and spread awareness.