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Introduction

As governments increasingly deploy AI in governance, the promise of efficiency is shadowed by ethical dilemmas and regulatory gaps. Ensuring accountability, fairness, and sovereignty becomes crucial to align AI use with democratic values and public interest.

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Ethical Challenges in AI Use

• Privacy Erosion and Data Misuse: AI systems rely on large datasets, risking misuse beyond original purpose.
  Eg: Welfare data being repurposed for policing.
• Lack of Informed Consent: Citizens often do not fully understand how their data is used.
  Eg: Low digital literacy in India leads to uninformed consent in data-sharing frameworks.
• Algorithmic Bias and Discrimination: AI can reinforce existing inequalities due to biased datasets.
  Eg: Facial recognition systems globally showing higher error rates for minorities.
• Exclusion and Welfare Denial Risks: Even minor AI errors can exclude vulnerable populations.
  Eg: Aadhaar-linked authentication failures denying welfare benefits.
• Labour Displacement and Ethical Trade-offs: Efficiency gains may translate into job losses without safeguards.
  Eg: Automation replacing lower-level administrative roles in governance systems.

Governance Challenges in AI Deployment

• Accountability Deficit in Decision-Making: Opaque AI systems make it difficult to assign responsibility.
  Eg: Hybrid public-private AI systems creating unclear liability chains.
• Over-reliance Without Clear Objectives: AI is often deployed without defining the problem it solves.
  Eg: Governments adopting AI without necessity or proportionality tests.
• Data Sovereignty and Security Risks: Dependence on private or foreign AI infrastructure risks control over national data.
• Regulatory Lag and Policy Gaps: Technology evolves faster than governance frameworks.
  Eg: India’s evolving AI guidelines are still adopting a largely “hands-off” approach.
• Vendor Lock-in and Market Concentration: Large partnerships can create long-term dependence on few companies.
  Eg: Risks of governments being tied to big AI firms like Anthropic in strategic deployments.

Safeguards for Responsible AI Deployment

• Necessity and Proportionality Test: Deploy AI only when essential and proportionate to the objective.
  Eg: Evaluating whether simpler alternatives exist before AI adoption.
• Privacy-by-Design Frameworks: Embed safeguards at the design stage rather than post-deployment.
  Eg: Data minimisation and purpose limitation principles in governance systems.
• Transparent and Explainable AI Systems: Ensure decisions can be audited and understood.
  Eg: Mandating explainability in AI-driven welfare or policing tools.
• Strengthening Public Sector Capacity: Invest in indigenous AI and scientific capabilities.
  Eg: India’s success in space programmes like ISRO built on core scientific investment.
• Independent Oversight and Regulation: Establish regulatory bodies and participatory governance models.
  Eg: Karnataka’s Committee on Responsible AI for ethical oversight.

Conclusion

AI in governance must remain a means, not an end. Ethical foresight, robust regulation, and citizen-centric safeguards are essential to ensure that technological advancement strengthens democracy rather than undermining rights, accountability, and public trust.

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