AI in public service

Artificial Intelligence platforms: Overview & analysis

Artificial Intelligence (AI) platforms are foundational technologies that support intelligent automation, data analytics, natural language processing (NLP), machine learning (ML) and decision-making tools. Understanding these platforms is essential from a governance and public policy perspective in the Civil Services Examination context.

1. OpenAI – ChatGPT

Developer: OpenAI (USA)

Specialisation: Natural Language Processing (NLP), Conversational AI

Strengths:

• Highly advanced in language understanding and generation.
• Useful in education, governance communication and content generation.
• Can draft reports, analyse policies, translate languages.

Limitations:

• May generate hallucinated or factually incorrect content.
• Black-box nature of large models; less interpretability.

Ethical concerns: bias, misinformation, job displacement.

Current performance: Leading in mainstream adoption across sectors like education, journalism, customer service.

Futuristic outlook:

• Integration in government services for citizen support.
• Potential in education, RTI response, court assistance.
• Needs strong AI regulation and ethical frameworks.

2. Google DeepMind – Gemini (formerly Bard)

Developer: Google DeepMind (UK/USA)

Specialisation: Multimodal AI, Search-Integrated AI

Strengths:

• Strong integration with Google’s ecosystem (Search, Maps, Gmail).
• Powerful for data-driven tasks, real-time factual lookup.

Limitations:

• Limited in creative generation compared to ChatGPT.
• Still evolving in its fine-tuning and UI/UX.

Current performance: Gaining traction due to integration with Google Workspace tools.

Futuristic outlook:

• Can revolutionise e-governance dashboards and automated data interpretation.
• Could assist bureaucrats with real-time policy comparisons and legal precedents.

3. IBM Watson

Developer: IBM (USA)

Specialisation: AI for enterprises, healthcare, legal analytics

Strengths:

• High accuracy in structured data analytics.
• Proven utility in public health, legal reviews, and financial governance.

Limitations:

• Expensive to implement and maintain.
• Less flexible in creative tasks like generative AI.

Current performance: Widely used in healthcare and business intelligence.

Futuristic outlook:

Can aid in digital health missions, smart city analysis, and public grievance redressal.

Needs alignment with local language data and policy databases.

4. Microsoft Azure AI

Developer: Microsoft (USA)

Specialisation: Cloud-based AI services, Cognitive Services

Strengths:

• Scalable, enterprise-grade services like facial recognition, translation, and speech synthesis.
• Used in public services and surveillance systems.

Limitations:

• Privacy and surveillance concerns.
• High cost for small governments or developing nations.

Current performance: Backbone of many digital services globally.

Futuristic outlook:

• Could power India’s Smart Governance via National Cloud Projects.
• Ethical AI policy and digital infrastructure upgrades are essential.

5. Amazon AWS AI (SageMaker, Lex, Rekognition)

Developer: Amazon Web Services (USA)

Specialisation: Machine Learning at scale, Vision AI, Conversational Interfaces

Strengths:

• Highly customisable.
• Excellent in image recognition and automated ML deployment.

Limitations:

• Complex for non-technical users.
• Data localisation and security concerns in public sector adoption.

Current performance: Dominates the cloud-based AI infrastructure.

Futuristic outlook:

• May enhance data analytics for disaster management, environment monitoring.
• Governments must ensure data sovereignty and usage regulations.

6. Meta AI (LLaMA models)

Developer: Meta (Facebook) (USA)

Specialisation: Open-source LLMs for research and academia

Strengths:

• Open-source approach promotes transparency and innovation.
• Useful for local language fine-tuning and democratic access to AI.

Limitations:

• Not yet user-friendly for mainstream or policy purposes.
• Potential misuse without safety nets.

Current performance: Used by researchers and universities.

Futuristic outlook:

• Can help in building India-specific AI for governance in vernacular languages.
• Needs regulatory oversight and support for ethical open-source development.

7. Perplexity AI

Developer: Perplexity AI Inc. (USA)

Specialisation: AI-powered search and answer engine

Strengths:

• Provides accurate, cited answers with web references.
• Real-time web browsing integration for up-to-date information.
• Focuses on factual correctness and transparency.

Limitations:

• Limited in creative or narrative text generation compared to ChatGPT.
• Dependent on internet connectivity and web indexing quality.

Current performance: Popular for research, academic queries, and fact-checking.

Futuristic outlook:

• Could support bureaucrats and policymakers with verified, citation-based data.
• Potential use in real-time policy analysis and RTI responses.
• Needs improved multilingual support for Indian governance needs.

8. GitHub Copilot

Developer: GitHub OpenAI (under Microsoft)

Specialisation: AI-driven code assistant; Auto-completion of code, bug fixing, code documentation

Strengths:

Boosts productivity of software developers, especially in public sector IT projects.

Useful for upskilling in digital India mission; supports multiple programming languages.

Encourages open-source contribution and quicker app development in governance.

Limitations:

• Sometimes suggests incorrect or insecure code.
• Relies on training data from open-source repositories — licensing and copyright issues have been raised.

Less useful for non-developer audiences.

Current performance: Widely used in tech companies, open-source projects and increasingly in ed-tech.

Futuristic outlook:

• Could be integrated in Digital India for faster development of citizen-centric portals.
• Potential to support AI-based teaching of programming in school curriculums under NEP 2020.
• Needs AI ethics and copyright law alignment in India.

Indian perspective: Bhashini & AIRAWAT

Bhashini (by MeitY): NLP platform to bridge language barriers in governance.

AIRAWAT (C-DAC): India’s supercomputing initiative to provide AI compute infrastructure.

Comparative snapshot

Performance & governance perspective

AI platforms are becoming crucial in public service delivery, data-driven policy and citizen engagement.

India’s AI strategy must:

• Encourage indigenous AI development.
• Ensure ethical safeguards.
• Promote digital literacy and inclusive access.

Future of AI will involve collaborative regulation, open innovation and citizen-centric design.

Futuristic perspective

1. AI as a bureaucratic assistant: Auto-summarising cabinet notes, drafting policies, tracking beneficiary schemes.
2. Judicial use: Supporting judges with precedents and speedy document analysis.
3. Disaster management: Predictive analytics to mitigate losses.
4. Public grievance redressal: Multilingual chatbots, fast redressal systems.
5. Education & skill development: Personalised learning and AI tutors for BharatNet-connected schools.

Conclusion

From a civil services perspective, understanding AI platforms is vital for enabling tech-driven governance, ethical leadership and balanced digital growth. As future administrators, one must assess how to harness AI’s potential while safeguarding constitutional values like inclusivity, transparency and accountability.