Corporate Governance, Technology, and the Law - Perspectives from China and India

As technology becomes integral to corporate functioning, companies’ governance must address two key dimensions: leveraging technology within governance structures and managing the risks it introduces. We call this ‘digital governance’ and in our recent working paper, we examine how China and India address it. Our analysis focuses on hard and soft law, policy statements, and corporate practices (using small empirical surveys).

By focusing our attention on China and India, we are addressing overlooked but significant perspectives in the AI race, and using the lens of corporate law and governance to do this. While US tech companies are seen to be at the forefront of this race, and the EU is seen to be regulating heavily and perhaps even exporting its regulations (an idea famously termed ‘Brussels effect’), recent advances in China have woken up the world to China being a formidable competitor. At the same time, other countries like India are looking to encourage AI development and use, and American big tech and other investors are keen to increase investments into India’s AI industry. 

We sought to answer three key questions in our paper: 

1. What regulatory approaches are China and India adopting to manage AI and data protection within the corporate governance framework? 

2. How are companies in China and India incorporating technological expertise into their corporate governance structures, and to what extent is this information being disclosed publicly? 

3. To what extent do institutional and developmental differences between China and India shape their respective responses to technology-related risks and innovation within corporate governance?

Our analysis not only answers these questions but also makes further reflections and conclusions. 

In answering the first and second research questions, our takeaway is that there is a convergence in corporate governance practices (if not laws and regulatory tools, which remain a product of institutional factors) around AI risks. We have seen higher AI adoption, as well as more laws and regulations on AI use and issues stemming from this use that have come before the courts in China than in India. In practice, the sorts of solutions to address the risks posed by AI use at the corporate level in both countries are not very different from what we see in the West—the use of Chief Technology Officers, a board committee to deal with tech risks or integrating tech risks into the ambit of the risk management committee, and disclosures of tech use and risk management of such use. While these take the form of top-down guidance and policy incentives from central and provincial authorities in China, they are simply corporate responses to market demands in India. 

In answering the third research question, we first note a point of similarity that stands out: both countries have national policies to encourage the development and adoption of AI by companies. China adopts a state-driven, pro-innovation regulatory model for AI governance that relies heavily on soft law tools such as guidelines, standards, and strategic plans. The government plays a central role in steering AI development through top-down coordination and policy incentives and guidance, rather than strict legal constraints. While China has a comprehensive legal and regulatory framework related to data protection, cybersecurity, and increasingly, AI-specific and algorithm-related rules, its overall approach remains flexible and adaptive, aiming to promote innovation while managing risks as they arise. This model contrasts with the EU’s more precautionary and rights-based regulatory approach.

While China seems to have many specific regulations and soft law in terms of managing risks, India has many fewer, and the reason for this might be a combination of four things. First, India is a little behind in AI adoption, and this might be one explanation why there is limited regulation on this issue. In other words, it might still be in a law-making phase. Second, India is attempting a more principles-based, flexible, and self-regulatory model of AI governance (although this contrasts with the ultra-mandatory approach adopted in the broader corporate governance context) as compared to China, which takes a rules-based regulatory model. Third, China, as a centralized state with strong state power, has a long-standing tradition of using the ‘invisible hand’ of governance—through policy documents, administrative guidelines, and strategic plans—to steer market behavior and shape industrial development without relying solely on formal legislation. Fourth, China, as a civil law country, relies heavily on written laws, administrative regulations, and formal guidelines to manage the risks associated with data protection and technology use. This legal tradition emphasizes codified rules and top-down regulation, resulting in a more structured and detailed regulatory framework. In contrast, India, as a common law country, tends to adopt a more principles-based and case-driven approach, with greater reliance on judicial interpretation and regulatory discretion. As a result, China has developed more comprehensive and prescriptive rules in areas like AI governance and data regulation compared to India’s more flexible and evolving model.

Beyond answering the research questions we began the paper with, we also express further reflections, two of which we discuss here. First, ESG concerns permeate AI regulation, policy and practice in both China and India. In China, central State-Owned Enterprises (SOEs) are promoting ecological collaboration and ESG practices through AI. In India, these sorts of practices have not emerged in the public or private sectors. However, it is worth noting here that the policy paper on AI (titled ‘National Strategy for Artificial Intelligence’) by NITI Ayog (akin to a planning commission) has the subtitle ‘#AIForAll’ and particularly notes that public sector undertakings in India should be ‘leaders in adoption of social AI tools’. This focus from both China and India on SOEs and public sector companies respectively is not surprising, considering that both are emerging economies with development imperatives. Second, our paper shows that the China and India stories must be accounted for in the broader global AI regulation narrative, not only because they are major jurisdictions with AI ambitions, but also because their experiences offer alternative narratives to the EU versus US regulatory trajectories. Further, other countries, particularly in the Global South where development needs inform AI ambitions, might seek to learn from the experiences of China and India.

Akshaya Kamalnath is an Associate Professor at the Australian National University Law School.

Lin Lin is an Associate Professor at the Faculty of Law of the National University of Singapore.