Algorithms in the Secretariat: Ethics of AI in Public Administration

Context: The Algorithmic State

Government use of AI has moved from pilots to production — Aadhaar-linked deduplication of beneficiaries, AI-flagged GST fraud, facial recognition at airports under DigiYatra, and predictive analytics in policing. Each promises efficiency; each also introduces the risk that an opaque model quietly decides who receives a ration card or who is stopped at a checkpoint.

Core Ethical Dilemmas

• Accountability gap: when a model wrongly excludes a beneficiary, responsibility diffuses between vendor, data team and signing officer — the 'many hands' problem.
• Bias and fairness: training data reflecting historical discrimination can encode caste, gender or regional bias into 'neutral' outputs.
• Transparency versus efficacy: fully explainable models may be less accurate; but citizens have a right to reasons under natural justice (audi alteram partem).
• Autonomy and consent: citizens often cannot opt out of algorithmic processing by the State.
• Deskilling: over-reliance erodes the officer's independent judgement — the Kantian duty to treat persons as ends, not data points.

A Civil Servant's Framework

Ethical use of AI in administration rests on treating the algorithm as an aid to, not a substitute for, reasoned decision-making. The officer remains the moral agent.

• Human-in-the-loop for all adverse decisions affecting rights or entitlements.
• Algorithmic impact assessments before deployment, published in the spirit of Section 4 of the RTI Act.
• Grievance redressal that allows a citizen to contest a machine-assisted decision and receive reasons.
• Procurement clauses requiring vendors to disclose training data provenance and audit access.