GS Paper 4 (Ethics)Ethics and AI in administration

The application of Artificial Intelligence as a dependable source of input for administrative rational decision-making is a debatable issue. Critically examine the statement from the ethical point of view.

Based on 42 evaluated answers to this question on CollectorBabu: here is what aspirants most often missed, the mistakes that cost marks, and a model answer.

What most aspirants missed

Accountability gap: who is liable when an AI-driven decision is wrong (developer, department or official), with the need for legal liability frameworks
Privacy and consent issues, citing Aadhaar data-security concerns and the Puttaswamy privacy judgment as a rights-based anchor
Transparency and right to explanation for black-box decisions, with explainable AI as a middle path
Bias amplification in criminal justice or recruitment, e.g. COMPAS racial bias (ProPublica) and Amazon CV screening, violating justice and fairness
Exclusion of vulnerable groups through the digital divide in welfare targeting, linked to distributive justice
Regulatory safeguards such as human oversight, algorithmic audits and the DPDP Act 2023 or EU AI Act

Common mistakes

Listing benefits and harms without analysing them through any ethical framework such as utilitarianism or deontology, so the 'critically examine' demand is not met
Describing AI in technical terms (data volume, processing speed) rather than linking it to ethical values like justice, dignity, privacy and accountability
Citing examples without explaining their ethical implication, for instance naming Cambridge Analytica without connecting it to consent or democratic integrity
Contradicting themselves, such as claiming AI removes bias and later showing it reproduces bias; the conclusion also merely restates the question

What the better answers did

Setting out a clear stance early and testing it against both benefits (efficiency, reduced favouritism) and ethical costs (bias, privacy, accountability)
Using specific Indian and global cases, such as Aadhaar, COMPAS, SECC welfare targeting and Singapore's governance framework, each tied to a named ethical principle
Proposing concrete safeguards like explainability, algorithmic audits, human-in-the-loop review and legal liability, showing the balance between technology and moral responsibility
Ending with a reasoned final standpoint that makes AI an aid to, not a substitute for, human administrative judgment

Introduction

Open by stating that AI can inform but should not replace administrative judgment, then signal that you will weigh its benefits against rights-based and utilitarian ethical concerns.

Conclusion

Close with a firm standpoint that AI is dependable only as a governed aid, tying transparency, accountability and human oversight back to the ethical core of public service.

Model answer

Artificial Intelligence promises speed, consistency and data-driven resource allocation in public administration. However, its reliability as a source of input for rational decision-making is contested on ethical grounds, because administrative decisions affect rights, dignity and access to welfare. Viewed through utilitarian and rights-based lenses, AI offers gains in aggregate welfare but creates risks to individual justice that cannot be ignored.

Case for AI as a dependable input

Reduces arbitrariness and human favouritism; rule-based scoring can curb corruption in licensing and tax assessment.
Enables data-driven targeting, as in DBT and SECC-based welfare identification, reducing leakages.
Fraud detection in GST and FASTag-based monitoring improves revenue integrity and public trust when used with safeguards.

Ethical concerns

Bias and justice: Models trained on historical data reproduce discrimination. The COMPAS recidivism tool was shown by ProPublica to over-flag Black defendants as high risk, violating the principle of equal treatment.
Transparency and right to explanation: Black-box models make it impossible for a citizen or court to know the basis of a decision. Explainable AI and the Singapore Model AI Governance Framework treat explainability as a precondition for trust.
Accountability gap: When an erroneous welfare exclusion or facial recognition error occurs, it is unclear whether the developer, the procuring department or the official bears responsibility. Legal liability frameworks are needed.
Privacy and consent: AI needs massive data. The Puttaswamy judgment (2017) recognised privacy as a fundamental right, and Aadhaar data-security concerns and the Cambridge Analytica episode show how data misuse erodes dignity. Citizens also do not consent to being governed by algorithms.
Digital divide and exclusion: Algorithmic welfare targeting can wrongly exclude genuine beneficiaries lacking digital footprints, violating distributive justice.
Human judgment: AI lacks moral reasoning and empathy needed in edge cases, such as COVID relief distribution where discretion is required.

Way forward

Mandatory algorithmic audits, human-in-the-loop review for adverse decisions, and explainable models.
Ethics committees and a statutory framework aligned with the DPDP Act, 2023 and emerging global norms like the EU AI Act.

AI can be a dependable input only when it is treated as an aid rather than an authority. Its ethical acceptability depends on transparency, accountability, privacy protection and human oversight, so the debate is resolved by governance design rather than by the technology alone.

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