The integration of AI into administration marks a shift from “Human Discretion” to “Algorithmic Governance.” However, its role as a “dependable source” is ethically complex as “rational” decision in administration is not just about logical consistency, but about justice and equity.
Ethical Case for AI as a Dependable Source
It reduces cognitive bias and offers Neutrality and Objectivity in decision making. Eg- use of AI in SSC exam evaluation
Data-driven policymaking promotes evidence-based governance. Eg- Aarogya Setu App
Ensures consistency and predictability – Eg- AI based custom approval process by DGFT
Efficiency in service delivery. Eg- Samagra Vedika platform of telangana create a “360-degree profile” of every citizen to verify eligibility for welfare schemes
By removing the “human interface”, AI reduces the opportunity for bribery and rent-seeking. Eg- AI-driven MCA21 V3 portal flags suspicious shell companies and fraudulent filings.
Enhances accuracy. Eg- IMD using AI for weather forcasting
Disaster Management Efficiency- Eg- Google’s AI Flood Forecasting model, used by CWC, provides real-time flood alerts
Targeted welfare delivery – AI improves beneficiary identification in DBT.
Utilitarian Optimization- Eg- NITI Aayog partnered with IBM to develop AI models for Crop Yield Prediction for farmers
Enhancing public participation through language inclusivity. Eg- BHASHINI platform
Ethical Concerns
The “Black Box” Problem- AI logic is often opaque.
Institutionalizing Historical Bias- If data is biased , the AI will “learn” and automate that bias. Eg- US COMPAS tool biased against African-Americans.
Difficulty in ensuring accountability for mistakes.
Loss of “Compassion” and “Conscience”- Eg- It cannot “bend the rules” for a starving widow whose paperwork is missing.
Digital Dehumanization- AI reduces complex human lives into “risk scores” or “eligibility percentages”.
Threat to Right to Privacy- Constant AI monitoring can turn a “Welfare State” into a “Surveillance State”.
Skill Atrophy- Administrators may stop using their own judgment, leading to a loss of critical thinking – “Steel Frame” to “Silicon Frame”
Widening North-South divide – Poor countries lack access to tech-mediated services.
Way Forward- “Human-Centric AI.”
Explainable AI- Implementing systems where the AI must provide a “human-readable” reason for every decision.
Human-in-the-Loop (HITL)- The final “sign-off,” especially in cases affecting human rights, must be by a human officer.
Mandating regular 3rd-party audits of government algorithms to detect and “unlearn” biases.
The EU AI Act Approach- “Risk-Based Framework” where high-risk AI (policing or judiciary) face the highest level of ethical regulation.
Digital Ethics Commissions including ethicists, jurists, and technologists to oversee AI deployment in public service.
Right to Appeal against AI- Statutory rights for citizens to have an AI-driven decision reviewed by a human committee.
Ethical Coding Standards- Teaching “Ethics by Design” to programmers working on public infrastructure.
Training civil servants in AI Literacy under Mission Karmayogi.
Strengthening privacy safeguards under DPDP Act.
The goal must be not AI-driven governance, but ethically guided AI-assisted governance.