Ethics of Technology, Data and Artificial Intelligence

Why technology ethics matters now

Digital technology has transformed governance in India – Aadhaar, UPI, DBT, DigiLocker, CoWIN, facial recognition at airports (DigiYatra), AI chatbots for farmers. These tools bring speed, scale and transparency. But they also raise new ethical questions: privacy, surveillance, exclusion, bias, accountability and misinformation. UPSC increasingly asks about AI, social media and data ethics, often in case studies. This lesson gives a framework.

Key ethical issues

1. Privacy and data protection

• Privacy is a fundamental right under Article 21 (*Justice K. S. Puttaswamy v. Union of India*, 2017, nine-judge bench).
• The Aadhaar judgment (2018) upheld Aadhaar for welfare and PAN linking but struck down its mandatory use by private companies (Section 57) and for bank accounts and mobile connections.
• Digital Personal Data Protection Act, 2023 – principles: lawful processing with consent (or "legitimate uses"), purpose limitation, data minimisation, accuracy, storage limitation, security safeguards and accountability; rights of data principals (access, correction, erasure, grievance redress, nominate); duties of data fiduciaries; a Data Protection Board of India; penalties up to ₹250 crore. The DPDP Rules were notified in 2025. Critics point to broad exemptions for the State.
• Ethical principles: informed consent, proportionality, minimum necessary data, security.

2. Surveillance

• Facial recognition by police, CCTV networks, phone interception (Section 5(2) of the Telegraph Act, now the Telecommunications Act, 2023), spyware allegations (Pegasus – the Supreme Court set up a technical committee in 2021).
• Test from *Puttaswamy*: any intrusion must satisfy legality (a law), legitimate aim, proportionality and procedural safeguards.

3. Algorithmic bias and fairness

• AI systems learn from past data and can reproduce bias – against women in hiring, against minorities in predictive policing, against the poor in credit scoring.
• Ethical requirement: test for bias, use diverse data, allow human review.

4. Transparency and explainability

• "Black box" algorithms make decisions that even designers cannot fully explain. Citizens denied a benefit by an algorithm must know why and be able to appeal.

5. Accountability

• Who is responsible when an AI system errs – the developer, the department or the officer? Principle: humans remain accountable; AI assists but does not replace responsibility.

6. Digital divide and exclusion

• Biometric authentication failures in ration distribution, lack of connectivity or smartphones, low digital literacy, disability barriers. Reports of people denied rations due to authentication failure led to offline exceptions.
• Ethical principle: no one should lose an entitlement because of technology; keep assisted and offline options.

7. Misinformation and deepfakes

• Fake news can incite violence (lynchings linked to WhatsApp rumours in 2018), distort elections and damage reputations.
• Deepfakes – AI-generated fake videos and audio. The IT Rules, 2021 (amended) require platforms to remove unlawful content and label synthetic media; the government issued advisories on deepfakes in 2023–24.
• Responses: fact-checking units, platform accountability, media literacy, quick official rebuttals.

8. Cyber security and ethics of hacking

• Ransomware attacks on hospitals (AIIMS Delhi, 2022), data leaks. Ethical duty of officials to protect citizens' data; CERT-In directions (2022) require reporting of incidents within 6 hours.

9. Platform and gig economy ethics

• Fair pay, social security and algorithmic management of gig workers; the Code on Social Security, 2020 recognises gig and platform workers; Rajasthan passed a gig workers' law in 2023.

10. AI and employment

• Automation may displace jobs; ethical duty to reskill workers and share gains.

Ethical frameworks for AI

Applying classical ethics to technology

• Kant: people must be treated as ends – no manipulation through dark patterns, no use of personal data without consent.
• Utilitarianism: weigh benefits (efficiency, reach) against harms (exclusion, errors) – but do not ignore minorities harmed by errors.
• Virtue ethics: developers and officials need honesty, prudence and responsibility.
• Gandhi's "science without humanity" – one of the seven sins – warns against technology that ignores human welfare.
• Rawls: design systems as if you could be the person most likely to be excluded.

Guidelines for an ethical digital administrator

1. Necessity and proportionality – collect only data that is needed.
2. Consent and notice – tell citizens what is collected and why.
3. Human in the loop – significant decisions reviewed by a human; right to appeal.
4. Inclusion by design – offline options, local languages, accessibility.
5. Bias audits and pilot testing.
6. Security – encryption, access controls, incident reporting.
7. Transparency – publish algorithms' purposes and performance where possible.
8. Accountability – clear responsibility for outcomes.

Worked example

A State plans to use facial recognition at railway stations to catch criminals.

• Benefits: faster identification, safety.
• Concerns: privacy, mass surveillance, false matches (higher for some groups), lack of a specific law.
• Ethical approach: a law or clear rules defining purpose; limiting use to serious crimes with judicial or senior oversight; accuracy testing; deletion of data of non-suspects; public disclosure; independent audit; grievance redress for wrongful stops.

This applies the *Puttaswamy* test and the principles of responsible AI.

India's digital public infrastructure: an ethical lens

India's digital public infrastructure (DPI) – Aadhaar (identity), UPI (payments), DigiLocker (documents), Account Aggregator (consented data sharing), CoWIN (vaccination) and ONDC (open commerce) – is built as open, interoperable public goods rather than private monopolies. This has ethical strengths: low cost, wide inclusion (UPI handles billions of transactions a month), competition and portability. The Account Aggregator framework is an example of consent-based data sharing – the citizen decides who can see financial data and for how long. At the same time, scale magnifies risk: a single data breach or exclusion error can affect crores of people. Hence DPI must be paired with strong data protection, grievance redress and independent oversight. India offered DPI as a global public good during its G20 presidency (2023).

Social media and civil servants

Officers increasingly use social media to communicate with citizens – sharing alerts, responding to grievances, countering rumours. Ethical guidelines include: keeping official and personal accounts separate; no political comments; no sharing of confidential information; respectful replies; correcting misinformation with verified facts; and avoiding self-promotion that turns public work into personal branding. Used well, social media builds trust and speed; used badly, it breaches neutrality and confidentiality.

Worked example: AI in welfare targeting

A State proposes an AI model to flag "ineligible" beneficiaries of a pension scheme using electricity bills, vehicle records and land data. Ethical steps: test the model for errors on a sample; treat flags as leads for verification, not automatic cancellations; give notice and a chance to respond before stopping any pension; publish the criteria; monitor whether the poorest groups are disproportionately flagged. This keeps efficiency gains while protecting the most vulnerable from algorithmic error.

Mains practice question

"Artificial intelligence promises efficient governance but threatens privacy, fairness and accountability." Discuss the ethical issues and suggest safeguards for the use of AI in public administration. (250 words)

Answer framework

• Uses of AI in governance (examples).
• Issues: privacy (Puttaswamy, DPDP), bias, black box, accountability, exclusion, deepfakes.
• Frameworks: NITI Responsible AI, UNESCO, EU AI Act.
• Safeguards: human in loop, audits, consent, offline options, transparency.
• Conclude: technology with humanity (Gandhi).