Artificial Intelligence and Ethics: Emerging Challenges for UPSC GS-4
I. ARTIFICIAL INTELLIGENCE: CONCEPTUAL OVERVIEW
1. Meaning of Artificial Intelligence
Artificial Intelligence refers to the capability of machines and computer systems to simulate human intelligence through algorithms and data. Unlike traditional software, AI systems can learn from experience, adapt to new inputs, and improve performance over time.
AI systems today influence governance, markets, security, and personal lives, making them ethically significant rather than merely technical tools.
Key functions include:
- Learning (Machine Learning)
- Reasoning and decision-making
- Natural Language Processing
- Image and speech recognition
- Autonomous action
Examples:
- Chatbots and recommendation engines (governance, e-commerce)
- Facial recognition (law enforcement)
- Autonomous vehicles (transport safety)
- Predictive policing (crime prevention)
- Medical diagnostics (public health)
II. WHY AI RAISES ETHICAL CONCERNS
AI is not value-neutral because:
- It is trained on human-generated data
- It reflects existing social biases and power structures
- Its decisions can directly affect rights, opportunities, and lives
Unlike human decision-makers, AI often lacks moral reasoning, empathy, and contextual understanding, raising ethical concerns regarding:
- Responsibility
- Fairness
- Transparency
- Human dignity
Thus, ethical governance of AI becomes essential in a democratic society.
III. ETHICAL ISSUES ASSOCIATED WITH AI
1. Bias and Discrimination
AI systems trained on biased or incomplete datasets may institutionalize discrimination at scale, making injustice systematic and less visible.
Examples:
- Facial recognition showing higher error rates for women and minorities
- Algorithmic hiring tools filtering out certain social groups
đź“Ś Ethical Values Affected: Justice, Equality, Fairness
2. Privacy and Surveillance
AI enables continuous data collection, profiling, and behavioural prediction, which can erode personal freedom if unchecked.
When surveillance becomes automated and predictive, it risks creating a chilling effect on civil liberties.
Examples:
- AI-enabled CCTV surveillance
- Social media data misuse
- Unauthorised biometric data use
đź“Ś Ethical Values Affected: Privacy, Autonomy, Liberty
3. Accountability and Responsibility
AI systems often operate as “black boxes”, making it difficult to explain or challenge decisions.
This creates a responsibility gap, where moral and legal accountability becomes diffused.
Example:
- Autonomous vehicle accident — unclear liability
đź“Ś Ethical Values Affected: Accountability, Rule of Law
4. Transparency and Explainability
Democratic governance requires decisions to be reasoned, explainable, and reviewable.
Opaque AI systems undermine:
- Due process
- Trust in institutions
- Informed consent
Example:
- AI-based credit scoring or welfare beneficiary selection
đź“Ś Ethical Values Affected: Transparency, Procedural Justice
5. Job Displacement and Economic Inequality
Automation can disproportionately affect low-skill and routine workers, leading to:
- Job insecurity
- Skill polarisation
- Social unrest
Without reskilling and social safety nets, AI may widen inequality.
đź“Ś Ethical Values Affected: Social Justice, Equity
6. Human Autonomy and Control
Excessive reliance on AI may lead to automation bias, where humans unquestioningly accept machine outputs.
This risks dehumanising decision-making, especially in justice and warfare.
Examples:
- AI deciding parole or bail
- AI-assisted battlefield targeting
đź“Ś Ethical Values Affected: Human Dignity, Moral Agency
7. Weaponisation of AI
Lethal autonomous weapons raise profound ethical concerns because machines:
- Cannot understand moral responsibility
- Cannot be punished or held accountable
đź“Ś Ethical Values Affected: Right to Life, Humanitarian Ethics
IV. AI AND CORE ETHICAL THEORIES (GS-4 VALUE ADDITION)
- Utilitarianism: AI justified if it maximises overall welfare, but risks sacrificing minority rights
- Deontological Ethics: Certain actions (mass surveillance, lethal AI) are inherently unethical
- Virtue Ethics: Emphasises the ethical intent and moral character of designers and policymakers
- Social Contract Theory: AI must align with democratically accepted norms and public consent
V. GLOBAL ETHICAL FRAMEWORKS ON AI
- OECD AI Principles: Human-centred, transparent, safe, and accountable AI
- UNESCO AI Ethics (2021): Rights-based, inclusive, gender-sensitive AI governance
- EU AI Act: Risk-based regulation banning unacceptable-risk AI systems
VI. AI AND ETHICS: INDIAN CONTEXT
Opportunities
- Efficient service delivery
- Targeted welfare
- Healthcare access
- Precision agriculture
Ethical Risks
- Digital divide
- Weak data safeguards
- Algorithmic exclusion
- Surveillance without oversight
Indian Initiatives
- NITI Aayog’s “AI for All”
- Responsible AI guidelines
- Digital Personal Data Protection Act, 2023
- Advocacy for human-centric AI
VII. PRINCIPLES OF ETHICAL AI
Ethical AI must be:
- Human-centric
- Fair and non-discriminatory
- Transparent and explainable
- Accountable
- Privacy-preserving
- Safe and secure
- Inclusive and accessible
VIII. WAY FORWARD (UPSC READY)
- AI-specific and risk-based regulation
- Ethics-by-design and bias audits
- Capacity building and ethics education
- Human-in-the-loop decision systems
- Global cooperation on AI governance
IX. UPSC ANSWER USAGE MAP
- GS-3: Technology, regulation, digital governance
- GS-4: Ethics, accountability, justice, case studies
- Essay: Ethics vs technology, human-centric development
- Interview: Innovation vs regulation, privacy vs security
X. CONCLUSION
Artificial Intelligence can greatly enhance governance and human welfare, but without ethical guardrails, it risks undermining justice, liberty, and dignity. The real challenge before policymakers is not technological adoption, but ethical governance, ensuring that AI remains a servant of humanity rather than its master.
