“AI is the future of government efficiency, transparency, and citizen service, but it must be used responsibly, with accountability and safeguards.” – Sundar Pichai

According to report by McKinsey, artificial intelligence can increase global GDP growth by 16 percent by 2030, questions remain about its fairness, potential biases, and the erosion of human responsibility in governance.

Application of AI for administrative rational decision making

Policy Formulation

1. Data-Driven Decision: Eg: AI predicts disease outbreaks by analyzing health data.
2. Policy Impact Simulation: Eg: AI models simulate the economic impacts of tax reforms.
3. Sentiment Analysis: Eg: AI tools gauge public opinion on social media regarding policy proposals.

Policy Implementation

1. Resource Allocation: Eg: Urban planners use AI to optimize public transport systems based on population growth predictions.
2. Service Delivery: AI automates routine tasks, improving responsiveness and efficiency. Eg: Ask Disha chatbot of Indian Railway.
3. Process Automation: Eg: AI automates document verification for passport applications.

Monitoring

1. Fraud Detection: Eg: AI in tax systems flags suspicious filings for further investigation.
2. Decision Support Systems (DSS): use for real-time monitoring and resource allocation. Eg: AI helps disaster management teams allocate resources efficiently during emergencies.
3. Transparency and Accountability: by documenting the rationale for decisions and maintaining audit trails. 

Ethical issues might arise as noted by National Strategy for AI (NSAI)-2018

1. Algorithmic Bias: Eg: Amazon’s recruitment AI was found to favor men over women.
2. Humanity: Virtual assistants (e.g., Alexa) may reduce human-to-human interaction, impacting social relationships.
3. Evil Genies: AI chatbots like Microsoft’s Tay inadvertently promoted hate speech due to poor programming.
4. Singularity: Worries about superintelligent AI surpassing human control, as seen in debates on AI safety.
5. Accountability: It’s often unclear who is responsible for AI-driven decisions—developers, administrators, or the system itself.
6. Transparency (Black Box Problem) – Eg: AI used in welfare programs might deny benefits without clear reasons, leading to lack of trust.
7. Fairness:  Eg: AI in public services could favor urban areas over rural regions due to data imbalances.
8. Privacy Issues: Eg: AI-based surveillance systems might misuse citizens’ private data for unintended purposes.
9. Autonomy: Eg: Automated decision-making in refugee applications may overlook individual circumstances, reducing human compassion.

Way forward

1. International collaboration – OECD AI Principles,Australia AI Ethics Guidelines, EU Artificial Intelligence Act can provide a guiding light in this regard.
2. Promoting inclusive AI which will prioritize equity, diversity and accessibility, benefitting all segments of society is very essential. Eg- Eg. RESPONSIBLE AI #AIFORALL of NITI Aayog.

Adherence to UNESCO’s ‘Recommendation on the Ethics of Artificial Intelligence’ and Ethical Impact Assessment (EIA) of AI projects is essential for Ethical use of AI.