Subject: Applied EthicsXTech

As per ITU, over 6 billion people globally and over 86% of households in India use the internet. This expansion of the internet has acted as a double-edged sword.

Emergence of New Cultural Norms in Conflict with Traditional Norms

Individualism vs. Collectivism – Eg- emergence of nuclear families and single parent parent families replacing joint families

Instant Gratification vs. Patience (Tapas) – Eg- rise in “Speed-Dating” apps and “Swipe Culture” conflicting with traditional marriage systems

Egalitarianism vs. Seniority – Youth questioning traditional family hierarchies or social structures based on information found online. Eg- rise in inter caste marriages

Transparency vs. Discretion – Eg- culture of “oversharing” personal lives (Eg- vlogging) conflicts with traditional value of “keeping family matters private”

Global vs. Ethnic Identity – Eg- dilution of local linguistic dialects and regional folk songs in favor of “standardized” global pop culture.

Fluid vs. Fixed Identity – fluid “career hopping” encouraged by the internet is seen as “instability” by traditionalists who value life-long commitment.

Materialism vs. Contentment – constant consumption and luxury displays in conflict with traditional values of simple living and “santosh.” Eg- “FOMO” (Fear of Missing Out) marketing.

Digital Nomadism vs. Rootedness – weakening of local community bonds due to high mobility among youth.

Crowd Wisdom vs. Institutional Expertise – Eg- diagnosing health issues via “Dr. Google” or Reddit threads instead of certified doctors

Moral Relativism vs. Absolute Codes. Eg- rising social tension regarding traditional views on gender roles, and diet.

Counter-Argument- Internet expansion strengthening traditional values.

Internet-driven “Wellness Culture” has expanded reach of Yoga and Ayurveda

Linguistic conservation. Eg- “People’s Linguistic Survey of India” use digital recording to preserve tribal dialects like Santhali or Tulu

Market Access for Traditional Artisans – Eg- Platforms like Etsy, Okhai, or GoCoop allow Kalamkari artists to sell directly to global markets

Live Streaming of temple Aartis (like Kashi Vishwanath) allows virtual religious participation.

Food bloggers and YouTube channels focused on “Village Cooking” have documented rare regional culinary techniques. Eg- Village Food Factory

Modernizing Traditional Education. Eg- Vedic Math courses on Ed-tech

Social Media as a Tool for “Cultural Assertion” – Eg- Trends like #SareeTwitter or #DhotiChallenge

Emerging digital “Sustainability” values align with ancient traditional concepts of minimal living. Eg- “slow fashion” brands like Ka Sha

The emergence of ‘glocalization’ represents the fusion of internet driven modernity and community-oriented wisdom of tradition.

“Technology is a useful servant but a dangerous master.” – Christian Lous Lange

Digital technology provides vast data infrastructure for modern governance, yet its reliability as an input for objective, rational decision-making remains highly controversial.

Digital Technology as a Reliable Source of Input for Rational Decision-Making

Real-Time Policy Inputs: Eg: CoWIN platform enabled real-time vaccine inventory planning across all Indian districts.

Citizen Participation in Policy- Eg- MyGov collected over 10 crore citizen suggestions that shaped the National Education Policy 2020.

Digitizing demographic and macro-surveys significantly eliminates human enumeration mistakes and calculation errors.

Satellite tracking and geospatial maps provide precise objective inputs for infrastructure projects. Eg: PM GatiShakti National Master Plan GIS data.

Leakage Elimination-

Interlinked digital architectures unify isolated departmental databases into a singular, holistic policy-making dashboard. Helps eliminate duplication.

Predictive Weather Planning: Eg: The IMD’s advanced Doppler radar systems supplying precise cyclone trajectory inputs to save coastal communities.

Counter-Argument: The Flaws and Risks of Digital Inputs

Poor data collection practices hampers objective policy making and implementation. Eg- Ghost Beneficiaries under Ayushman Bharat.

The “Black Box” Problem- AI logic is often opaque.

Institutionalizing Historical Bias- If data is biased , the AI will “learn” and automate that bias. Eg- US COMPAS tool biased against African-Americans.

Difficulty in ensuring accountability for mistakes.

Correlation vs Causation Fallacies: Analytical engines can link two completely unrelated data trends together, generating irrational choices.

Exclusion of the Digitally Illiterate from policy making & online grievance portals

Way Forward

Human-in-the-Loop (HITL)- The final “sign-off,” especially in cases affecting human rights, must be by a human officer.

Mandating regular 3rd-party audits of government algorithms to detect and “unlearn” biases.

The EU AI Act Approach- “Risk-Based Framework” where high-risk AI (policing or judiciary) face the highest level of ethical regulation.

Digital Ethics Commissions including ethicists, jurists, and technologists to oversee AI deployment in public service.

Ethical Coding Standards- Teaching “Ethics by Design” to programmers working on public infrastructure.

While digital technology streamlines administrative efficiency, it cannot replace human empathy, requiring a balanced model where data informs but conscience rules.

“Technology is a useful servant but a dangerous master.” – Christian Lous Lange

While online methodologies offer paperless, and boundary-less world, they simultaneously create a “digital wall” of exclusion.

Advantages and disadvantages

Ethical issues for vulnerable sections

Digital exclusion – “digital apartheid” for rural students during COVID.

Privacy risks – Eg- Aadhar data breach

Children face mental health issues due to increased screentime, exposure to child pornography etc

Informed consent erosion – Eg- Vulnerable users often click “Agree” without understanding data-sharing terms.

Surveillance concerns -Constant AI monitoring can turn a “Welfare State” into a “Surveillance State”.

Difficulty in ensuring accountability for mistakes.

Loss of “Compassion” and “Conscience”- Eg- It cannot “bend the rules” for a starving widow whose paperwork is missing.

Dependency exploitation – Eg- Middlemen charge to fill forms.

Cyber fraud – Eg- rise of digital arrest cases

Cultural barriers – Language issues in online platforms.

Disability access – Apps not accessible to visually impaired.

Psychological Distress- The “Digital Divide” causes a sense of “techno-inferiority” and alienation among the elderly and rural populations.

Online methodologies should be a bridge, not a barrier. The way forward is not to reject technology, but to adopt a “Phygital” (Physical + Digital) approach.

The integration of AI into administration marks a shift from “Human Discretion” to “Algorithmic Governance.” However, its role as a “dependable source” is ethically complex as “rational” decision in administration is not just about logical consistency, but about justice and equity.

Ethical Case for AI as a Dependable Source

It reduces cognitive bias and offers Neutrality and Objectivity in decision making. Eg- use of AI in SSC exam evaluation

Data-driven policymaking promotes evidence-based governance. Eg- Aarogya Setu App

Ensures consistency and predictability – Eg- AI based custom approval process by DGFT

Efficiency in service delivery. Eg- Samagra Vedika platform of telangana create a “360-degree profile” of every citizen to verify eligibility for welfare schemes

By removing the “human interface”, AI reduces the opportunity for bribery and rent-seeking. Eg- AI-driven MCA21 V3 portal flags suspicious shell companies and fraudulent filings.

Enhances accuracy. Eg- IMD using AI for weather forcasting

Disaster Management Efficiency- Eg- Google’s AI Flood Forecasting model, used by CWC, provides real-time flood alerts

Targeted welfare delivery – AI improves beneficiary identification in DBT.

Utilitarian Optimization- Eg- NITI Aayog partnered with IBM to develop AI models for Crop Yield Prediction for farmers

Enhancing public participation through language inclusivity. Eg- BHASHINI platform

Ethical Concerns

The “Black Box” Problem- AI logic is often opaque.

Institutionalizing Historical Bias- If data is biased , the AI will “learn” and automate that bias. Eg- US COMPAS tool biased against African-Americans.

Difficulty in ensuring accountability for mistakes.

Loss of “Compassion” and “Conscience”- Eg- It cannot “bend the rules” for a starving widow whose paperwork is missing.

Digital Dehumanization- AI reduces complex human lives into “risk scores” or “eligibility percentages”.

Threat to Right to Privacy- Constant AI monitoring can turn a “Welfare State” into a “Surveillance State”.

Skill Atrophy- Administrators may stop using their own judgment, leading to a loss of critical thinking – “Steel Frame” to “Silicon Frame”

Widening North-South divide – Poor countries lack access to tech-mediated services.

Way Forward- “Human-Centric AI.”

Explainable AI- Implementing systems where the AI must provide a “human-readable” reason for every decision.

Human-in-the-Loop (HITL)- The final “sign-off,” especially in cases affecting human rights, must be by a human officer.

Mandating regular 3rd-party audits of government algorithms to detect and “unlearn” biases.

The EU AI Act Approach- “Risk-Based Framework” where high-risk AI (policing or judiciary) face the highest level of ethical regulation.

Digital Ethics Commissions including ethicists, jurists, and technologists to oversee AI deployment in public service.

Right to Appeal against AI- Statutory rights for citizens to have an AI-driven decision reviewed by a human committee.

Ethical Coding Standards- Teaching “Ethics by Design” to programmers working on public infrastructure.

Training civil servants in AI Literacy under Mission Karmayogi.

Strengthening privacy safeguards under DPDP Act.

The goal must be not AI-driven governance, but ethically guided AI-assisted governance.

Code of Ethics

Over 6 Billion people worldwide and 491 million people in India use social media. However, this expansion of social media has acted as a double-edged sword.

Social Media Revolutionizing Communication and Interaction

Elimination of Geographical Barriers- Platforms like WhatsApp and Zoom have turned the world into a “Global Village”.

Shift from Passive to Active Participation- allows users to be “Prosumers” (consumers and creators of content)

Democratization of Voice- Eg- #BlackLivesMatter or #MeToo movements

The rise of Emojis, GIFs, and Memes has created a new universal “digital dialect” that conveys emotion more quickly than text.

Civic and Political Engagement- Eg- leaders using X for communication

Ethical Issues and Challenges

Privacy and Surveillance challenge- Eg- the Cambridge Analytica scandal.

Algorithmic Echo Chambers creating polarized “digital silos”. (India Hate Lab report)

Cognitive Warfare- Eg- “Bot-farms” to build anti-India narratives

Cyber Stalking and Harassment – Eg- Women targeted through doxxing and revenge porn.

Digital Addiction- Eg- “Infinite Scroll” syndrome

Post Truth world- Eg- Deepfakes make it difficult to distinguish between reality and fabrication.

Key ethical dilemmas in social media

Freedom of Speech vs. Content Moderation- Balancing an individual’s right to speak against the community’s right to be protected from hate speech.

Privacy vs. Profit- The tension between protecting user data and the platform’s need for targeted advertising revenue.

Connectivity vs. Isolation- The paradox of being “connected” to thousands online while feeling deeply isolated in the physical world.

Accountability vs. Anonymity- Anonymity protects whistleblowers but also shields those who commit harassment without consequence. Eg- rise of “Deepfake Extortion”

Authenticity vs. Curation- The pressure to present an “idealized self” online versus the ethical value of being true to one’s real-life identity.

Information Accessibility vs. Intellectual Property- The ease of sharing leads to dilemmas regarding “Plagiarism” and the loss of revenue for original creators.

Democratization vs. Polarization- While everyone has a voice, algorithms often push people into “Filter Bubbles” and “Echo Chambers”.

Algorithmic Bias vs. Social Justice – Eg- recruitment algorithms on major professional networking sites were less likely to show high-paying technical job ads to women

Accuracy vs. Speed (The Viral Trap) – In the “attention economy,” being the first to break a story is rewarded with “reach,” while slow fact-checking is penalized by the algorithm.

Social media is an ethically “grey” space. A robust framework of “Digital Ethics”is needed to cultivate “Digital Mindfulness.”

Environmental Ethics