Artificial Intelligence in Governance: Transforming India’s Public Administration

Syllabus: GS2/ Governance

Context

• India’s increasingly complex governance difficulties demand a shift from fragmented and periodic administration to AI-enabled, real-time, predictive and evidence-based government.

Current Goverannace issues

• Fragmentation of governance architecture: Government departments operate thru separate databases, reporting formats and monitoring mechanisms, which constrain information integration and undermine whole-of-government approach.
• Delayed Monitoring: Excessive reliance on periodic reports and reviews may lead to late detection of project delays, financial leakages, cost overruns and gaps in public-service delivery.
• Departmentalisation of Decision-Making: Although issues such as health, agriculture, energy, and welfare and employment are closely interrelated, policies are often made and evaluated in isolation in individual departments.
• Limited Real-Time Visibility: Policymakers suffer from a lack of real-time information and are unable to detect emerging issues and act before they become larger administrative or fiscal problems.

Significance of AI in Governance

• Predictive Governance: AI-driven predictive models can spot potential problems like disease outbreaks, infrastructure delays, unusual spending trends, and resource shortages, allowing for timely preventive actions.
  • Example: The Integrated Disease Surveillance Programme (IDSP) can leverage digital disease-surveillance data and AI-based analytics to identify emerging disease trends and support early public-health interventions.
• Better Public Service Delivery: AI is able to improve targeting and delivery of welfare schemes by detecting duplication, exclusion and matching public resources to emerging needs.
  • Example: Aadhaar-enabled data systems and data analytics can help identify duplicate or ineligible beneficiaries in welfare programmes
• Improving Fiscal Management: Artificial Intelligence can help in detecting unusual financial transactions, lags in fund utilisation and impending fiscal stress and hence, enhance the management of expenditure and reduce possible leakages.
• Integrated Governance: AI may assist to analyse information across departments, so that policymakers can understand the interlinks across different sectors and take a whole-of-government approach.
  • Example: The PM GatiShakti National Master Plan integrates data from multiple ministries and infrastructure sectors on a common GIS platform, helping improve coordinated infrastructure planning.
• Evidence Based Decision Making:  AI based analytics can help policymakers make data based decisions and reduce the over reliance on late reports and subjective judgements.

Challenges

• Poor data quality causes unreliable AI output. Inaccurate, incomplete or outdated government records can compromise the effectiveness of predictive systems.
• Algorithmic bias, when artificial intelligence systems are trained on unrepresentative data, can reinforce existing social or administrative biases.
• Digital divide: It can marginalise citizens who do not have access to internet, electronic literacy, or digital devices. AI-enabled services should therefore complement physical and human channels of service delivery.
• Institutional capacity: AI governance requires trained staff, strong digital infrastructure, cybersecurity capabilities and appropriate regulatory structures.

Way Ahead

• Build interoperable governance platforms: Government agencies should use common data standards and interoperable digital systems to allow secure and legal exchange of information between agencies and levels of government.
• Improve data quality: The effectiveness of AI is highly dependent on the quality of underlying data. Governments should ensure that public datasets are accurate, updated, standardised and representative.
• Employ a human-in-the-loop approach: AI should support administrative decision-making and not replace human judgement, especially when decisions touch upon welfare entitlements, public services and individual rights.
• Cybersecurity: AI-enabled governance should adhere to India’s data-protection framework and include strong safeguards to prevent data misuse, unauthorised access and cyberattacks.
• Ensure algorithmic accountability: Regular audits on bias, accuracy and explainability of artificial intelligence systems used in governance should be conducted with clear mechanisms for human review and grievance redressal.

Source: TH