Q12 · UPPSC PCS Mains 2025 · GS IV · 12 marks · ~200 words in the hall · 2 min read

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In the age of digital governance and big data, public officials rely increasingly on algorithms and automated decision-making systems. Critically examine the ethical risks involved in algorithmic bias, opacity and lack of accountability and suggest guiding principles for responsible use of such technologies in public administration.

Topic: Ethics in Public Administration. Syllabus: Public/Civil Service values and ethics in Public Administration. Same official PYQ from year-wise 2025 and Ethics in Public Administration.

Revision summary

Digital tools can hide bias, a black box, and an empty chair when a citizen is denied. Bias often copies old inequality in training data. Opacity leaves both the claimant and the officer unable to explain a no. Accountability requires a named human owner of the order. Principles are legality, human review of high-stakes denial, fairness tests, plain appeal, data minimisation, open vendors, and redress.

Model answer

Introduction

Digital governance can speed a ration or a police list, and it can also hide a wrong in code. Bias, opacity, and no owner of the error are the three ethical failures that need principles, not only more software.

Body

The three problems

  • Algorithmic bias is systematic skew: a credit, welfare, or policing model can punish a caste, gender, language, or neighbourhood because the training data already did.
  • Opacity is the black box: a claimant cannot know why a portal said no, and often the officer cannot explain it either.
  • Lack of accountability is the empty chair: vendor, department, and algorithm each point at the other when a widow loses a pension.

Harm in public office

  • A facial mismatch at Aadhaar authentication can deny food without a human hearing.
  • Predictive lists can stigmatise a locality as criminal without a charge.
  • Automated moderation or targeting can silence a legitimate protest while leaving rumour untouched.

Guiding principles

  • Legality first: no automated decision should exceed what statute allows a human officer to do.
  • Human in the loop for high-stakes denial of life, liberty, livelihood, or reputation, with a spoken or written reason.
  • Fairness by design: test models for disparate impact before scale, and keep a manual override that is itself audited.
  • Explainability in plain language: the citizen gets the rule, the data used, and the appeal path, not a slogan about artificial intelligence.
  • Data minimisation and purpose limitation, in line with the Digital Personal Data Protection Act, 2023.
  • Public procurement transparency: the vendor, the training source, and the change log are on a file that RTI can reach, subject to lawful secrets.
  • Audit and redress: independent technical audit, a named grievance officer, and compensation when the code was wrong.
  • Dignity: no ranking of citizens as risk scores on a public wall.

Institutional habit

  • The officer remains the moral owner of the order even when a screen suggested it.
  • Digital India is a delivery tool; it is not a shield against Article 14 and Article 21.

Flow diagram

flowchart TD
  B[Bias] --> P[Fairness test]
  O[Opacity] --> E[Plain reason]
  A[No owner] --> H[Human officer]
  P --> T[Trust]
  E --> T
  H --> T

Conclusion

Bias, opacity, and no accountability are governance failures in code. The principles are law, a human hearing, fairness tests, plain reasons, data limits, open procurement, and a named owner of the error.

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