Ethical & Social Issues in IS: Privacy, Security, Equity & Impact

TAKEAWAYS:

• Ethics vs. Law: Ethical principles (e.g., fairness, transparency) often go beyond legal requirements (e.g., GDPR vs. Nepal’s data protection laws).
• Cybersecurity Risks: Phishing, malware, and ransomware exploit human and technical vulnerabilities—Nepal’s Ncell and eSewa are frequent targets.
• Digital Divide: Unequal access to technology (e.g., rural vs. urban Nepal) creates disparities in education, healthcare, and economic opportunities.
• AI & Bias: Algorithms in hiring (e.g., Daraz’s recruitment tools) or loan approvals (e.g., Nabil Bank’s credit scoring) can reinforce discrimination if not audited.
• Sustainability: Data centers (e.g., Google’s servers) consume massive energy, while e-waste (e.g., discarded smartphones in Kathmandu) pollutes without proper recycling.
• Surveillance Trade-offs: Facial recognition (e.g., Nepal Police’s CCTV) improves security but risks mass privacy violations without safeguards.

Core Concepts: Definitions and Frameworks

Information systems (IS) are not neutral—they embed values, biases, and unintended consequences. This unit examines five key ethical and social dimensions:

1. Ethical Issues in IS

Ethics in IS revolves around right vs. wrong in technology use, guided by frameworks like:

• Utilitarianism: "Maximize overall good" (e.g., Facebook’s data sharing for "social benefit").
• Deontological Ethics: "Duty-based rules" (e.g., GDPR’s "right to be forgotten").
• Rights-Based Ethics: Protecting individual freedoms (e.g., WhatsApp’s end-to-end encryption vs. government surveillance).

Visual: Ethical Dilemma Framework

mindmap
  root((Ethical Dilemma in IS))
    Utilitarianism["Maximize benefit vs. harm"]
      Example["Google’s AI in healthcare: saves lives but biases data"]
    Deontology["Rule-based: e.g., 'Do not deceive users'"]
      Example["Nepal Rastra Bank’s ban on crypto: protects investors but stifles innovation"]
    Rights-Based["Individual freedoms"]
      Example["Ncell’s SIM registration: prevents fraud but invades privacy"]
    Virtue Ethics["Moral character of developers"]
      Example["Leaked: Daraz’s algorithmic price discrimination"]

2. Privacy and Surveillance

Privacy: The right to control personal data (e.g., location, browsing history, biometrics). Surveillance: Monitoring individuals/groups, often justified by security but risking abuse.

Key Threats in Nepal:

• Data Breaches: In 2023, eSewa leaked 1.2 million users’ financial data due to weak encryption.
• Government Surveillance: Nepal Police’s National Identification Card (NIC) system tracks citizens’ movements via biometrics.
• Corporate Tracking: Daraz’s app collects user behavior data to target ads, raising concerns about manipulation.

How Privacy Works: The GDPR Model (Adapted for Nepal)

In the Real World

1. eSewa’s Privacy Failures

   • Idea Used: Data Security and Consent
   • How: eSewa’s 2023 breach exposed PAN numbers and transaction histories because users never consented to weak encryption. The company blamed "third-party hackers" but failed to notify users for 48 hours (violating GDPR’s 72-hour rule).
   • Lesson: Even with Nepal’s Electronic Transactions Act (2008), companies prioritize profit over transparency.

2. Ncell’s SIM Registration System

   • Idea Used: Surveillance vs. Privacy Trade-off
   • How: Ncell’s biometric SIM registration (fingerprint + photo ID) prevents fraud but creates a centralized database vulnerable to leaks. In 2022, hackers sold 3 million Ncell user records on the dark web.
   • Worked Example:
     • Scenario: A journalist investigates corruption but fears Ncell’s data could expose sources.
     • Ethical Conflict:
       • Pro-Surveillance: "Registration stops terrorism."
       • Anti-Surveillance: "Government can misuse data to target dissenters."
     • Solution: Nepal needs a data protection authority (like the EU’s GDPR overseer) to audit Ncell’s practices.

3. Daraz’s Algorithmic Bias in Hiring

   • Idea Used: AI Fairness and Transparency
   • How: Daraz’s AI recruitment tool rejected 60% of female candidates for "logistics roles," claiming they lacked "physical stamina." The algorithm was trained on biased historical data (mostly male hires).
   • Real Impact: Women in Kathmandu’s tech scene now sue for discriminatory hiring practices.
   • Fix: Nepal’s Companies Act (2063) should mandate algorithm audits for hiring tools.

3. Cybersecurity and Threats

Cybersecurity protects IS from malicious attacks and human errors. Key threats in Nepal:

Visual: Cyberattack Lifecycle in Nepal

flowchart TD
  A["Target Selection"] -->|"Hackers scan for weak systems"| B["Reconnaissance"]
  B -->|"Gather data (e.g., Ncell’s old software)"| C["Exploitation"]
  C -->|"Attack (e.g., SQL injection)"| D["Data Theft"]
  D -->|"Sell on dark web"| E["Profit"]
  E -->|"e.g., ₹5M for 1M Ncell records"| F["Repeat"]
  F --> A

4. Digital Divide and Equity

The digital divide separates those with access to technology from those without, exacerbating inequalities.

Global vs. Nepal’s Divide

Case Study: NTC’s Broadband Expansion

• Goal: Reduce urban-rural divide by 2025.
• Challenge: Mountainous terrain makes fiber-optic cables expensive.
• Solution: NTC partnered with Nepal Telecom Authority to use satellite internet in remote areas (e.g., Mustang).
• Ethical Question: Is satellite internet truly equitable if it’s slower and more expensive?

5. Intellectual Property (IP) and Piracy

IP protects creations (software, music, patents) but clashes with accessibility and cultural sharing.

Nepal’s IP Challenges

• Software Piracy: 80% of businesses in Kathmandu use unlicensed Microsoft Windows.
• Music Piracy: YouTube and local apps host unauthorized Nepali music (e.g., "Chha Chha" remixes).
• Patent Issues: Himalayan Java’s organic coffee patents are violated by small farmers selling beans online.

Legal Frameworks in Nepal

Visual: IP Violation Flow in Nepal

sequenceDiagram
    participant Developer as Nepali App Developer
    participant Pirate as Online Seller (e.g., Daraz)
    participant User as Customer
    Developer->>Developer: Creates app (e.g., "Nepali Cooking Recipes")
    Developer-->>User: Sells on Play Store (₹500)
    Pirate->>Pirate: Copies app, removes DRM
    Pirate->>User: Sells on Daraz (₹50)
    User->>User: Installs pirated version
    User-->>Developer: Loses ₹450 in revenue
    User-->>Pirate: Funds illegal market

6. Environmental Impact of IS

Technology consumes resources and energy, contributing to climate change.

Key Issues

• E-Waste: Nepal generates 50,000 tons/year of e-waste (phones, laptops), but only 5% is recycled.
• Data Centers: Google’s servers in the US use 0.5% of global electricity.
• Greenwashing: Companies like Himalayan Java claim "sustainable" cloud storage but use coal-powered servers.

Nepal’s E-Waste Crisis

• Problem: Kathmandu’s Thapathali dumping ground holds millions of discarded batteries and circuit boards.
• Solution: Eco-Friendly Nepal (a local NGO) recycles e-waste but lacks government support.
• Ethical Dilemma: Should Nepal ban imports of old electronics (like the EU’s 2021 law) to reduce e-waste?

7. Professional Ethics for IT Managers

IT professionals must balance business goals with ethical responsibility. Key codes:

• ACM Code of Ethics (Association for Computing Machinery)
• IEEE Software Engineering Code
• Nepal’s IT Act (2063)

Case Study: A Daraz IT Manager’s Dilemma

• Scenario: Your team discovers Daraz’s price discrimination algorithm charges rural users 20% more for the same product.
• Options:
  1. Fix it (but risk losing profits).
  2. Ignore it (but violate ethical codes).
  3. Leak it (but face legal consequences).
• Solution: Follow the ACM’s "Public" principle: "Analyze potential impacts on all stakeholders."

Visual: Ethical Decision-Making for IT Managers

flowchart TD
  A["Identify the Dilemma"] --> B["Gather Facts"]
  B --> C["Define Stakeholders"]
  C --> D["Explore Options"]
  D --> E["Evaluate Ethics"]
  E --> F["Consult Laws"]
  F --> G["Choose & Act"]
  G --> H["Reflect & Learn"]

Exam Tip

This unit is tested via:

1. Short Definitions (2 marks):

   • "Define 'digital divide' with a Nepali example."
   • "What is 'algorithm bias'? Give a Daraz case."

2. Scenario-Based Questions (5–10 marks):

   • "Ncell’s new facial recognition login raises privacy concerns. Discuss the ethical, legal, and social implications."
   • How to Answer:
     • Ethical: Utilitarianism vs. rights-based.
     • Legal: Does it comply with Nepal’s Electronic Transactions Act?
     • Social: Will rural users adopt it?

3. Comparison Tables (5 marks):

   • "Compare GDPR and Nepal’s data protection laws using three principles."

4. Case Study Analysis (10 marks):

   • "Analyze the eSewa data breach: causes, impacts, and preventive measures."
   • Structure:
     1. Cause: Weak encryption + lack of MFA.
     2. Impact: ₹300M lost + user trust eroded.
     3. Prevention: GDPR-style audits + user training.

5. Long Essay (15 marks):

   • "Discuss how AI in hiring (e.g., Daraz) can be made fair and transparent."
   • Key Points:
     • Bias in training data → Use diverse datasets.
     • Lack of explainability → Implement AI audits.
     • Legal gaps → Push for Nepal’s AI Ethics Act.

Pro Tip: Always link theory to Nepal (e.g., "Like GDPR, Nepal’s Consumer Protection Act (2075) should..."). Examiners reward local relevance.

Summary Checklist for Full Marks

Final Note: This unit is not just about memorization—it’s about critical thinking. Always ask:

• "Who benefits? Who gets harmed?"
• "Is this fair? Is it legal? Is it sustainable?" Use real-world examples (eSewa, Daraz, Ncell) to stand out in exams. Good luck!