The concept of Digital Public Agents (DPA) is designed to significantly enhance the capabilities of the public sector as it adapts to the emerging intelligent age. DPA is built upon several foundational assumptions:
- A new framework is essential for the public sector to utilize AI effectively and influence societal changes.
- AI solutions can yield greater benefits in the public sector if we reform underlying processes.
- By re-engineering approximately 20 fundamental government processes, we can revamp most public services.
- The synergistic potential of AI and Digital Public Infrastructure (DPI) can be greatly amplified.
- DPA merges the public utility of DPI with the transformative potential of AI to enhance service delivery and democratize access to AI technologies.
The two primary focuses of ‘HOW’ include:
- Integrating DPI attributes into the AI ecosystem.
- Designing and developing Digital Public Agents.
1. Designed for Public Good at Scale
- Utilize existing national DPI frameworks.
- Key components include:
- Digital Identity
- Digital Payment
- Consent-based Data Exchange
- Geo-Spatial Data Layers
- Focusing on scalable use cases aligned with Sustainable Development Goals (SDGs).
2. Strong Public Oversight and Accountability
- Implement a multi-stakeholder governance framework for:
- Policy Development
- Architectural Governance
- Responsible AI Usage
3. Reusable, Modular Building Blocks
- DPA must be inherently modular and reusable.
- Elemental DPAs should be crafted as modular components.
4. User-Centric Focus
- Develop Event-driven DPAs.
- Assess user needs through targeted surveys.
5. User Control of Personal Data
- Design DPAs to enable maximum autonomy based on informed consent.
6. Privacy by Design
- Prioritize minimal data collection and use.
- Ensure DPAs that use personal data comply with legal standards.
7. Openness
- Promote transparency through:
- Open processes for DPA design
- Open source development
- Open sources and components
- Open AI Models
8. Interoperability
- Establish open standards and protocols for integration across systems.
- Develop open APIs and AI protocols (MCP, A2A, ACP).
- Choice of Foundation Model: Utilize open-source Specialized Language Models (SLMs) that are tailored using comprehensive government-related documents for enhanced interaction and coordination.
- Federated Architecture: Deploy a series of 8-10 open-source SLMs specialized in various domains, ensuring these models are trained within their respective fields like Economic Development, Taxation, Welfare, and more.
- Integration with Legacy Applications: Facilitate integration either centrally or at a nodal level based on legacy architecture.
- Security and Privacy Management: Maintain security protocols in a federated manner across central and nodal levels.
- DPA Sandbox: Create a central sandbox for validating DPAs to enhance trust among users.
- Model & Tools Repository: Develop a centralized repository for architects and developers of the DPA ecosystem.
- Specialization: Design DPAs to either address common domain functions or focus on specific processes for optimal performance.
- Autonomy: Embed limited autonomy into DPAs to manage exceptions beyond predefined policies.
- Use Case Approach: Base design and training of Models on actual use cases.
- Local Language Interface: Leverage local language capabilities to enhance public service delivery.
This article is part of a five-part series on Digital Public Agents, with the fourth article dedicated to showcasing two practical use cases demonstrating the transformative potential of DPAs in public service delivery.
Authored by Mr. J Satyanarayana, a former member of the Indian Administrative Service and former Chairman of UIDAI (Aadhaar).
Disclaimer: The views expressed are those of the author and do not necessarily represent ETCIO. ETCIO is not liable for any direct or indirect damage caused to organizations or individuals as a result of this content.
NOTE: This is the third article in a series of five on Digital Public Agents.
Link to Article 1
Link to Article 2
