A Tale of Two Futures
In a bustling village in Karnataka, Ramesh, a small shop owner, uses his Aadhaar-linked UPI app to access a quick loan for expanding his business. Meanwhile, across the globe, in a small African nation, Ayo, a shopkeeper, struggles with an AI-driven loan system controlled by a private tech giant. The system denies her loan application without explanation.
Ramesh thrives because India’s DPI ensures transparency, inclusivity, and affordability.
Ayo, however, is at the mercy of a closed system prioritizing corporate interests over public welfare.
This contrast highlights the critical choice nations face today: DPI for AI or alternative proprietary systems.
Introduction
As artificial intelligence (AI) continues to transform industries, its integration with Digital Public Infrastructure (DPI) is becoming increasingly crucial. DPI forms the digital backbone that enables governments and businesses to deliver essential services efficiently and at scale. When combined with AI, DPI can significantly enhance governance, financial inclusion, healthcare, and data-driven decision-making.
This article explores the relationship between AI and DPI, how AI-powered DPI can transform public service delivery, and real-world examples showcasing this powerful synergy.
What is Digital Public Infrastructure (DPI)?
DPI refers to a set of open, interoperable digital systems that enable the delivery of essential services to citizens, businesses, and governments. DPI is built on three foundational pillars:
DPI provides a structured digital ecosystem for efficient service delivery, reducing friction in governance and private-sector innovations.
What is AI’s Role in DPI?
AI serves as a powerful enabler that can enhance the efficiency, accessibility, and intelligence of DPI systems. Here’s how AI enhances each component of DPI:
Why AI Needs DPI
AI systems require a robust and structured digital foundation to operate effectively. Without a well-integrated Digital Public Infrastructure (DPI), AI lacks the necessary data pipelines, governance frameworks, and interoperability to deliver meaningful, large-scale benefits. DPI ensures that AI models can function in a transparent, inclusive, and efficient manner, reaching the most underserved communities while fostering innovation.
Case Study: Kisan Mitra – AI-Powered DPI in Agriculture
One powerful example of AI-driven DPI is Kisan Mitra, an initiative that helps farmers in India make data-driven decisions. By integrating AI with DPI components such as Aadhaar, UPI, and DEPA, Kisan Mitra provides farmers with real-time insights on weather conditions, pest outbreaks, and crop advisories. With access to personalized AI-powered recommendations, farmers can take preventive measures, improving their yield and financial security.
For instance, a grape farmer in Maharashtra receives an AI-generated SMS warning about an upcoming pest infestation. He follows the recommended pesticide application, preventing damage to his crops. Additionally, his Aadhaar-linked account ensures that he receives an automated insurance payout in case of unexpected losses. This is the impact of AI working within a robust DPI ecosystem.
AI systems require structured, high-quality, and standardized data to function efficiently. DPI provides the necessary digital infrastructure to collect, store, and share data securely, enabling AI to generate meaningful insights and automate decision-making. Without DPI, AI development and adoption face significant barriers.
Comparing AI Implementation: With and Without DPI
The Alternative to DPI for AI: Proprietary Systems
While Digital Public Infrastructure (DPI) offers an open, interoperable, and inclusive framework for AI, the alternative is proprietary AI systems controlled by private corporations. These systems often operate in a closed ecosystem, where access to data, decision-making processes, and benefits are dictated by a handful of technology firms. Governments and citizens have limited control, leading to challenges in transparency, affordability, and accessibility.
Proprietary AI models rely on privately owned datasets, where economic incentives prioritize profit maximization over public good. Such systems are often designed for specific, high-value user segments, excluding those who are less profitable. This can result in exclusionary financial services, biased healthcare decisions, and AI governance that does not align with public interest.
The Risks of Proprietary AI Systems
Without a publicly governed DPI, AI development and data control could become centralized in the hands of a few large technology companies, leading to several risks. Proprietary AI systems often operate as opaque black boxes, where decision-making processes remain undisclosed. This lack of transparency can result in unfair outcomes, such as AI-driven loan rejections without justification. Additionally, profit-driven AI models tend to prioritize commercially viable users, leaving marginalized communities underserved or completely excluded from essential services.
Another significant risk is data exploitation, where private companies monetize user data without adequate safeguards, compromising privacy and security. Countries that rely heavily on proprietary AI systems risk becoming overly dependent on a handful of large tech corporations, diminishing national digital sovereignty. Furthermore, these systems often come with high licensing fees and vendor lock-in, increasing costs and limiting government flexibility in deploying AI-driven public services.
Without a publicly governed DPI, AI development and data control could become centralized in the hands of a few large technology companies, leading to several risks:
Conclusion
DPI is essential for ethical, inclusive, and sustainable AI development. Without it, AI remains controlled by private corporations, exacerbating inequality, data exploitation, and monopolization. Countries that invest in AI-ready DPI can ensure that AI serves public interest while driving innovation.
