By Subhodeep Jash & Sahil Deo
AI systems in a pandemic-struck but information-powered age are central to geopolitical and geo-economic power. AI is not a separate sector, but is about use-cases or applications across industries such as mobility, education, health, or even a language model that generates poetry and prose (GPT-3).
The AI race has been traditionally pitted as a US vs China face-off, from the time a machine owned by Alphabet Inc beat humans at the traditional game of ‘Go’. That moment led to the unveiling of an ‘AI Development Plan’ in China. However, trade tensions between the US and China have precipitated fears of a “digital Berlin Wall”, precipitating the need to re-examine global governance models in technology.
China’s social construct around AI- dominance is founded on its Great Firewall, a sophisticated surveillance apparatus, and other similar digital authoritarianism signages, whether it is its facial recognition technology or the social credit system.
Disruption and competitiveness on AI must be shaped in a more diffused manner, through collaboration among a set of responsible states. The answer perhaps lies in looking towards the Asia Pacific region, which has already made an early embrace of AI, spurred by “mobile-first” consumer markets. Countries like Australia, Japan, and Singapore have made significant investments and have drawn up ambitious national-level plans around AI.
Earlier this month, India launched the CANI (Chennai—Andaman and Nicobar Islands) submarine optical fibre cable (OFC) project, spanning over 2,300 km in the Indian Ocean region (IOR). At the unveiling of this landmark digital infrastructure pathway, PM Modi said that he envisages the Andaman Islands as a hub for IOR economic and strategic cooperation. Along with the US, countries in the IOR could forge a “Deep Tech” coalition, given their shared values and mutual interests on the geopolitical and geo-economic fronts.
The wing of AI technology that draws the most salience and excitement, whether in companies or governments, is machine learning (ML)—particularly neural networks. Put simply, ML is where algorithms train on data, discover patterns and predict future outcomes based on learnings from the training data-sets.
The AI stack mainly comprises four elements: data, algorithm, computing power, and talent/skilled pool. Currently, most of the emphasis on governance models is placed on the first two elements: data and algorithms.
China’s ascendance in the AI realm is concentrated on dominance exercised in three of the four layers described above—data, algorithms, and talent. Indeed, the Chinese government has laid out plans to be the world leader in AI by 2030, referring to this technology as ‘a new engine of economic development’.
China doesn’t collect large amounts of data from just its citizens, but as we have seen, from several states, whether India or the US, which have pushed back against the security risks of China’s domestic and foreign companies allegedly acting as conduits of the Chinese state’s surveillance apparatus.
How should one look at shaping a democratic technological model of AI governance? While countries may be at varying stages of development with regard to their AI value chains, definite synergies are waiting to be tapped. India is a massive data generator, with significant human capital that produces the second-highest STEM graduates globally, whereas, on data and computing, American companies hold considerable sway.
A counter to the hegemonic tendencies of China’s Belt and Road Initiative was drawn up through notable mechanisms such as the quadrilateral security group, known as ‘the Quad’—Australia, India, the US and Japan—which was built around connectivity, sustainable development and cybersecurity.
Australia’s efforts on AI were highlighted in its 2018 digital economy strategy, christened “Australia’s Tech Future”, that laid out a roadmap for public-private collaborations around emerging technologies. Japan has been among the first countries to develop a national AI strategy and has focused on strengthening R&D capabilities and systems around industrial applications.
For India, the emphasis has been on looking at AI as a vehicle of economic transformation. India’s national strategy for AI highlights the potential of how it could boost India’s annual growth by 1.3% by 2035. Two recent developments, a non-personal data governance framework and a responsible AI strategy document, also place a sharp focus on data availability for the domestic ecosystem.
All the four Quad powers have distinct yet compatible vantage points on AI adoption, and they could benefit from regional cooperation by leveraging better integration to seize the AI potential for addressing developmental challenges. We must look at AI governance in a vein that counterbalances the dominance of China. One such forum announced in June is the Global Partnership on Artificial Intelligence (GPAI) with the G7 member countries along with the four members of the quadrilateral.
All of these four major economies in the Indo-Pacific region have their strengths and capabilities on the AI stack’s varied aspects, whether it is data, infrastructure, investment or talent. The endgame, however, lies in whether we can genuinely forge an Indo-Pacific AI stack where the rubber meets the (Digital Silk) road in terms of political will, economic output, and maximising strategic outcomes.
Jash is a policy professional, FTI Consulting and Deo is co-founder, CPC Analytics. Views are personal
