Mains Practice Questions

Approach:

• Introduce the answer by briefing about advent of Artificial Intelligence and automation in public administration
• Delve into Key Applications of AI and Automation in Governance
• Highlight Institutional and Procedural Challenges
• Give Measures to Tackle Challenges of AI and Automation Integration in Public Services
• Conclude suitably.

Introduction:

The integration of Artificial Intelligence (AI) and automation into public administration is revolutionizing governance in India. A recent milestone, the Paris AI Action Summit, co-chaired by India and France, exemplified India’s pivotal role in shaping the global AI narrative around governance.

Body:

Key Applications of AI and Automation in Governance:

• Enhancing Policy Formulation and Decision-Making: AI enables data-driven decision-making, where vast datasets help predict socio-economic trends and optimize resource allocation.
  • For example, NITI Aayog's collaboration with IIT Delhi to develop an AI tool for predicting socio-economic conditions using satellite images.
• Strengthening Public Service Delivery: AI-driven automation improves service efficiency, reduces delays, and minimizes human error, especially in grievance redressal systems and welfare disbursements.
  • The India Urban Data Exchange (IUDX) under the Smart Cities Mission is an AI-powered data-sharing platform aimed at enhancing urban governance.
• Improving Law Enforcement and Internal Security: AI tools such as facial recognition and predictive policing are helping law enforcement agencies track criminal activities and enhance public safety.
  • The Delhi Police’s use of AI in facial recognition systems helps identify criminals and locate missing persons.
• Revolutionizing Healthcare and Pandemic Management: AI-driven tools for diagnostics, robotic surgeries, and disease surveillance are improving health outcomes and enabling timely intervention.
  • Startups like Niramai are leveraging AI to detect breast cancer early, demonstrating AI’s potential in healthcare.
• Optimizing Agricultural Productivity: AI-based precision farming techniques are improving crop yields, optimizing irrigation, and detecting pests.
  • Tools like ‘Kisan e-Mitra’, an AI-powered chatbot, are providing farmers with real-time information about government schemes.

Institutional and Procedural Challenges:

• Institutional Challenges:
  • Lack of AI Literacy and Skills: According to a report by NITI Aayog, only 22% of firms in India use AI in any business process, showing the widespread gap in AI adoption across the country.
    • To keep pace with technological advancements, there is a pressing need for rapid reskilling initiatives, as AI-driven automation is projected to displace 75 million jobs in India by 2025, according to a World Economic Forum study.
  • Data Privacy and Security Concerns: AI systems rely heavily on large datasets, raising privacy concerns. Key datasets like Aadhar are highly vulnerable.
    • A study by the Indian Institute of Technology (IIT) suggests that over 1.2 billion people’s data is stored in the Aadhaar system, presenting a significant risk if not safeguarded properly.
  • Algorithmic Bias and Ethical Challenges: AI’s potential to perpetuate biases is well-documented globally.
    • For instance, Amazon's AI-based recruitment tool was discontinued after it was found to be biased against female candidates.
      • This is not an isolated case. A report by AI Now Institute highlights that many AI algorithms used in governance could inadvertently replicate caste, gender, and regional biases.
  • Regulatory and Legal Gaps: India is still working towards a comprehensive AI regulatory framework.
    • A recent OECD report pointed out that India lacks specific AI laws like the European Union’s AI Act.
    • This leaves a significant legal vacuum in AI adoption, making it unclear who is accountable when AI errors lead to unfair outcomes.
• Procedural Challenges:
  • Infrastructure and Technology Gaps: According to NSSO data, only 24% of rural households in India have access to the internet, compared to 66% in urban areas.
    • This digital divide makes it difficult for AI-driven governance models to reach all citizens equitably, particularly in rural and underserved regions.
    • Additionally, India’s reliance on foreign AI technologies, such as cloud services and advanced chips, further highlights the infrastructure gap.
  • Resistance to Change in Bureaucratic Systems: Traditional bureaucratic systems are often slow to adopt new technologies, including AI.
    • There is a significant resistance to change due to entrenched work cultures, hierarchical structures, and fear of job displacement among public servants.
  • Data Accessibility and Interoperability Issues: AI requires vast and diverse datasets to function effectively. However, there is a lack of centralized, standardized datasets across various government departments, making it difficult to integrate AI tools across the entire public administration system.
    • Different government departments often maintain disparate data silos, leading to interoperability issues.

Measures to Tackle Challenges of AI and Automation Integration in Public Services:

• Enhancing AI Literacy and Reskilling Programs: India must invest in training centers, universities, and online platforms to enhance AI literacy among public sector employees and citizens.
  • The National AI Portal can be expanded to offer free online courses on AI for government officials.
  • Foster collaborations between tech companies and academic institutions, as seen with NITI Aayog’s partnership with IIT Delhi for AI-driven socio-economic predictions
• Strengthening Data Privacy and Security Framework: Amend the Digital Personal Data Protection Act to specifically address AI-related concerns, such as ensuring transparency in algorithmic decision-making and setting clear guidelines for AI-driven surveillance.
  • Promote data localization, where sensitive data, especially personal or biometric data, is stored within India. This will mitigate risks from foreign data breaches and enhance privacy.
  • Establish AI ethics committees in government departments, responsible for regular audits of AI systems to ensure compliance with data privacy norms and prevent the misuse of personal data.
• Mitigating Algorithmic Bias and Ensuring Fairness: Develop and implement robust frameworks for bias detection in AI systems.
  • Ensure that AI models are trained on diverse and representative datasets.
  • Initiatives like the Bhashini project can be expanded to create inclusive language models, accounting for the diversity in India’s population.
  • Mandate the use of Explainable AI (XAI) tools, which can provide transparency in AI decision-making processes.
• Developing a Comprehensive AI Regulatory Framework: India must expedite the creation of a National AI Act, similar to the EU's AI Act, with provisions for AI classification, accountability, ethical use, and transparency in governance.
  • This will help mitigate risks associated with AI adoption, such as wrongful decision-making or misuse of AI for surveillance.
  • Introduce regulatory sandboxes where AI-based solutions can be tested in real-world scenarios under relaxed regulations to ensure their safety.
    • Similar to the RBI's FinTech sandbox, such an initiative could facilitate experimentation in sectors like healthcare, taxation, and law enforcement.

Conclusion:

AI holds immense potential to transform governance in India. The framework for AI in governance should prioritize 3Ps: Protection of privacy, ensure Progress in service delivery, and foster Participation from all stakeholders to create an inclusive and transparent system.