Category: Public Administration, Governance, Public Policy

Transforming Governance with a Unified AI Stack

Source: Generated through AI by the author

With rapid advancements in artificial intelligence (AI), organisations are scrambling to implement the technology in their business processes and service delivery frameworks to improve efficiency and enhance citizen experiences.

AI is set to impact nearly every sector, but to harness its potential, organisations need an ‘AI-first’ strategy that includes scalable, flexible AI solutions for business transformation. This requires an integrated AI stack — comprising infrastructure, data, AI models, and applications — enabling AI deployment across various use cases. Can such an AI stack be developed as a digital public infrastructure (DPI) by the government to provide seamless, proactive services to citizens and businesses?

To create a DPI, it is essential to understand the components of an enterprise-level AI stack. The foundation of this stack is a compute infrastructure layer, which includes compute capacity, storage, networking and tools for developing, training and deploying AI models. This layer would utilise Graphics Processing Units (TPUs), Central Processing Units (CPUs) and Tensor Processing Units (TPUs) optimised for AI workloads. Cloud platforms offer scalability, while edge computing may be necessary for real-time services in remote or low-bandwidth environments.

The second layer consists of the data layer, which focuses on collecting, storing, cleaning and annotating data for use by the AI models. Data security and compliance with the privacy laws must be ensured through encryption, anonymisation and access control. Data comes from various sources like structured and unstructured databases, web, Internet of Things (IoT), Application Programming Interfaces (APIs), etc. It must be cleaned and prepared for the AI model training to enhance accuracy and fairness. Ministries and departments have created huge databases under the Digital India programme that can be shared to train AI models for delivering predictive and proactive services to citizens and businesses.

The next layer is the model development layer, which focuses on designing and training models on the processed data from the data layer to address specific use cases, such as text or image/video generation, predictive analytics, etc. This involves selecting suitable AI frameworks, libraries, algorithms for the type of AI tasks involved, their optimisation and validation. Many open-source options, including pre-trained foundational models, can be customised for specific domains. However, developing indigenous foundational models is crucial to ensuring strategic autonomy and creating world-class capabilities within the Indian technology ecosystem. This doesn’t need to be resource-intensive, as demonstrated by DeepSeek.

The developed AI model is then deployed or exposed through APIs or microservices enabling integration with the enterprise systems, web and mobile applications. Next comes the application layer, which integrates AI models into real-world systems to deliver AI-enhanced products and services. This may involve reengineering business processes, automating tasks and redesigning user interfaces. For example, an AI application for predictive analytics might generate advance warnings for heavy traffic at specific locations during peak hours and send automated alerts for immediate action.

Finally, the AI stack also needs to have a governance layer to ensure that the associated risks, if any, are managed and trust is built in the AI systems. The government’s IndiaAI Mission should focus on creating a common AI stack as DPI, which all ministries and departments can use to build their own AI applications. This will prevent duplication of efforts and resources and create a vibrant innovation ecosystem focused on transforming public services with an ‘AI-First’ strategy. The AI stack could also be made available to startups and the private industry to promote collaborative development and deployment of AI applications.

(The above article appeared in The Economic Times on February 9, 2025. It is available at https://economictimes.indiatimes.com/tech/artificial-intelligence/transforming-governance-with-a-unified-ai-stack/articleshow/118073623.cms?from=mdr. The views expressed are personal.)

Writing the New Rules for AI

Regulating AI

Source: https://www.istockphoto.com/photos/ai-regulation

The launch of ChatGPT in November 2022 heralded a new era in democratizing the use of artificial intelligence (AI). Since then, use of AI has quickly expanded across many sectors, including healthcare, education, financial services, public safety, etc. However, rapidly advancing capabilities of AI have also brought to the fore the criticality of safety and ethical use of these technologies. At the Global Partnership for Artificial Intelligence summit in 2023 in New Delhi, the Hon’ble Prime Minister stressed the importance of creating a global framework for ethical use of AI, including a protocol for testing and deploying high-risk and frontier AI tools. Earlier, at the first global AI Safety Summit 2023 at Bletchley Park, 28 countries gave a call for international cooperation to manage the challenges and risks of AI.

How can a global framework for safe and ethical use of AI be developed? Several countries have initiated efforts to regulate and govern AI. The US government issued an executive order in October 2023, focusing on safe, secure and trustworthy development and use of AI. It seeks to address several critical areas, including national security, consumer protection, privacy, etc. and requires AI developers to share safety results with the US government. EU’s AI Act adopts a risk-based regulatory approach with stricter oversight for higher levels of risk of the AI systems.

At a fundamental level, a global framework for governance of AI must address the key concerns regarding development, deployment and use of AI. These include dealing with machine learning biases and potential discrimination, misinformation, deep fakes, concerns on privacy and access to personal data, copyright protection, potential job losses, and ensuring the safety, transparency and explainability of the AI algorithms.

The goal of AI governance should be to promote innovation and ensure safe, fair and ethical applications of the technology in promising sectors. To address the concerns noted above, the framework for governance of AI must be based on certain core principles, which can be enumerated as below.

Innovation: The governance framework must promote innovation and competition in AI technologies to continuously improve them. This would require, for example, facilitating access to large amounts of anonymized datasets to startups for developing and training AI applications in various domains. The National Data Governance Policy of GoI is an excellent initiative in this direction.

Infrastructure: The framework must also support expanding access to compute infrastructure and AI models to promote competition and encourage innovation. This would particularly be helpful to startups in this domain.

Capacity Building and Engagement: A sustainedfocus on capacity building holds the key to involving and engaging with more stakeholders in the development and deployment of AI across multiple sectors. This can significantly help in managing and reducing the risks. Engaging with stakeholders would also help in addressing any potential job losses and worker displacements due to deployment of AI.

Safety and Risk Management: This would involve development of standards and ensuring that AI models are tested and assessed for safety and risk. Appropriate risk management strategies must be put in place to address any likely harms that may be caused. This would include ensuring transparency, fairness and explainability in the AI development lifecycle through selection of proper training data sets, removing any biases and ensuring that cybersecurity issues have been addressed.

Privacy Protection: AI models must focus on privacy preserving technologies to ensure protection of privacy. This would help in creating trust in these models and enhancing their beneficial impact.

International Cooperation: For any global framework to succeed, international collaboration and partnerships built on a shared vision and common goals are essential. A global framework on AI must build on evidence in this rapidly evolving technology and promote collaboration across all countries to become effective.   India, being a global leader in technology, can play a proactive role in developing a global framework for governance of AI based on the key principles enumerated above. With its huge technology talent base and a rapidly growing economy, India enjoys a unique advantage in the global technology ecosystem, which it can leverage in this direction. We also need to focus on the development of AI applications trained on Indian data sets in various domains, such as agriculture, education, health care, transportation, public safety, etc., which can play a huge role in revolutionising the entire citizen-centric service delivery paradigm and bring efficiency gains at a systemic level across multiple sectors.

(The above article appeared in The Economic Times on January 28, 2024. It is available here: https://economictimes.indiatimes.com/tech/catalysts/writing-the-new-rules-for-ai/articleshow/107192031.cms?from=mdr. The views are personal.)

Reforms in Frontline Bureaucracy Hold the Key to Better Public Services

Recently, the government has launched a number of initiatives aimed at spurring economic growth and improving the delivery of public services in the country. The ambitious ‘Make in India’ and ‘Digital India’ programmes are aimed at achieving these goals. In this context, a number of steps have been initiated to reform the bureaucracy to ensure better performance and accountability. However, these reforms are mostly aimed at higher and middle level bureaucrats with the assumption that reforming these layers would automatically translate into better performance from the frontline public servants who are at the cutting edge of service delivery and come in direct contact with the citizens, e.g., in tehsil, block and panchayat offices, public distribution system outlets, primary health centres, etc.

Can public policy goals be achieved without paying adequate attention to improving the working conditions and performance of these frontline public servants? These workers interact directly with the citizens in the course of their work and exercise substantial discretion in the execution of their responsibilities. The way they behave and perform significantly affects the delivery of services and perception of quality of governance in the minds of the people. The actions of these workers effectively become the public policies being implemented by the government. It can be said that public policy is actually made in the daily activities of these workers. They often exercise discretion in carrying out their responsibilities, giving rise to agency issues in their performance. However, their actual performance is heavily dependent on their working conditions. They are often faced with low resources and high expectations from both the public and their superiors. It may also be difficult to measure their performance as goal expectations may be vague or conflicting. They may also not be receiving adequate training or guidance in implementing new policies or rules and their incentives may not be aligned with the changes that are sought to be enforced from the top. This issue is particularly relevant in the age of information and communication technologies (ICTs) where they are expected to become IT-savvy to help in implementing a number of citizen-centric e-governance services. 

Therefore, it is clear that if public policy is to become effective, frontline workers must be taken fully on board. We must recognize that focusing on frontline workers offers an alternative, bottom-up approach to improving implementation on the ground. They should be central to any reforms. For example, if the delivery of health services is to improve significantly, the frontline ASHA workers must dramatically improve their performance.  How can these public servants be made to perform better and held accountable?

First, we must recognize that frontline workers can be a source of great innovation and administrative entrepreneurship by properly channeling the flexibility and discretion that they enjoy. Providing them with adequate resources and building their capacities to function effectively in the present IT age can encourage and motivate them to innovate in their work and become effective instruments for implementing public policy on the ground. Improving capacities and harnessing their local knowledge in improving the citizen interface will also make the work more meaningful for the workers themselves.

Secondly, empowerment and participation of citizens in governance and demand side pressures from the end users are crucial in improving the performance of these workers. As these workers directly interact with the citizens, participation of citizens and end-users can help in effectively improving the quality of service delivery and accountability.

Thirdly, government must aim at using ICTs effectively in all domains at the cutting edge of service delivery to ensure that services are provided uniformly and according to pre-defined quality standards to all citizens. The Digital India programme aims, inter alia, at precisely this goal and must be made a cross-cutting endeavour across all government ministries and departments. Capacity building of the frontline workers in deploying ICTs holds the key to improving public service delivery. For example, if ASHA workers can be trained to use a hand held tablet to collect data on patients and diseases in villages and transmit them in real time so that timely treatment could be planned and delivered through the primary health centres, it would revolutionize public health care system in the country.

Fourthly, it is often seen that these workers routinize their work and ration the services as a way of coping with inadequate resources, capacity and pressures. Regular audits on the quality of service delivery can be an effective way to reducing the discretion of these workers in the implementation of public policies and delivery of services. It is evident that the actions of frontline workers constitute a key part of how the overall quality of governance is perceived by the people. They also play a vital role in reaching ‘hard to reach’ groups, such as those living in remote villages or tribal communities. It is important that we recognize the need for reforming this layer of bureaucracy so that implementation of public policies and delivery of services can be improved from ‘bottom-up’.  We must focus the content of reforms at these workers to bring in effective change in the quality of governance on the ground.

The above article was originally published as “Take Frontline Workers on Board to Make Public Policy Effective” in Deccan Chronicle on March 18, 2016. The views are personal.