Business Ethics 101: Ethical Dilemmas and Case Studies - Technology Ethics Algorithmic Bias Surveillance AI Ethics Social Media Harms

Technology Ethics: Algorithmic Bias, Surveillance, AI Ethics, Social Media Harms

What This Is

Technology ethics examines the moral implications of digital tools—AI, algorithms, surveillance, and social media—on individuals, societies, and businesses. It matters because unethical tech harms trust, fuels discrimination, and triggers regulatory backlash (e.g., fines, bans). Example: Volkswagen’s "Dieselgate" (2015) used software to cheat emissions tests, but modern scandals involve AI—like Amazon’s hiring algorithm (2018), which discriminated against women by favoring male résumés, or Facebook’s role in the Rohingya genocide (2017), where its algorithms amplified hate speech.

Key Theories & Frameworks

• Utilitarianism (Bentham/Mill): Maximize net benefit for the greatest number. Relevance: Justifies AI-driven efficiency (e.g., predictive policing) but fails if it harms minorities (e.g., biased facial recognition).
• Deontology (Kant): Duties and rules matter more than outcomes. Relevance: Demands transparency in AI (e.g., "right to explanation" under GDPR) and bans manipulative design (e.g., dark patterns in apps).
• Virtue Ethics (Aristotle): Focus on moral character (e.g., integrity, courage). Relevance: Encourages tech leaders to prioritize "ethical by design" (e.g., Microsoft’s AI ethics board) over profit-driven shortcuts.
• Justice as Fairness (Rawls): Fairness requires impartiality and protecting the vulnerable. Relevance: Exposes algorithmic bias (e.g., COMPAS recidivism tool over-predicting Black defendants’ risk) and demands equitable access to tech.
• Care Ethics (Gilligan): Relationships and empathy guide decisions. Relevance: Counters cold utilitarianism in AI (e.g., IBM’s AI for healthcare prioritizing patient well-being over cost-cutting).
• Stakeholder Theory (Freeman): Businesses must balance interests of users, employees, communities, and regulators. Relevance: Forces companies to consider harms like TikTok’s teen mental health crisis or Palantir’s surveillance contracts with ICE.
• Ethics of Responsibility (Jonas): Tech’s power demands proactive accountability. Relevance: Justifies "precautionary principle" (e.g., EU’s AI Act banning high-risk uses like social scoring).
• Corporate Digital Responsibility (CDR): Extends CSR to tech, covering data privacy, AI ethics, and digital inclusion. Relevance: Guides companies like Salesforce (ethical AI principles) or Google (abandoning Project Maven over employee protests).

Step-by-Step Decision Process

Use the Ethical Tech Decision Model (ETDM)—a hybrid of Kidder’s checkpoints and Nash’s 12 questions:

1. Frame the Issue
2. Ask: What’s the tech’s purpose? Who’s affected? What are the risks (bias, privacy, harm)?

3. Example: Deploying facial recognition in retail stores to track shoppers.

4. Gather Facts

5. Check: Accuracy rates (e.g., NIST found facial recognition 10–100x worse for Black and Asian faces), legal constraints (e.g., Illinois BIPA), and stakeholder impacts (e.g., false arrests, chilling effects on customers).

6. Apply Ethical Theories

7. Utilitarian: Does the benefit (e.g., theft prevention) outweigh harms (e.g., misidentification, surveillance creep)?
8. Deontological: Does it violate rights (e.g., privacy, consent)? Is it reversible (e.g., opt-out options)?

9. Justice: Does it disproportionately harm marginalized groups? (e.g., Clearview AI’s database of 3B photos, mostly of non-consenting people).

10. Test for Traps

11. Ask: Are we rationalizing ("It’s just data")? Slipping into surveillance capitalism? Ignoring long-term consequences?

12. Consult Stakeholders

13. Engage: Employees (e.g., Google’s AI ethics walkouts), users (e.g., Twitter’s algorithmic bias surveys), and regulators (e.g., FTC’s AI guidance).

14. Decide & Document

15. Choose: Deploy with safeguards (e.g., Microsoft’s facial recognition principles) or abandon the tech.
16. Record: Justify the decision (e.g., "Deontological duty to protect privacy outweighed utilitarian benefits").

Common Ethical Traps

• Trap: "Move Fast and Break Things"
• Prevention: Adopt responsible innovation frameworks (e.g., IEEE’s Ethically Aligned Design). Why? Speed without ethics leads to scandals (e.g., Cambridge Analytica).
• Trap: Algorithmic Neutrality Fallacy
• Prevention: Audit for bias (e.g., IBM’s AI Fairness 360 tool). Why? Algorithms reflect human biases (e.g., Apple Card’s gender bias in credit limits).
• Trap: Surveillance Capitalism
• Prevention: Limit data collection (e.g., DuckDuckGo’s privacy-first model). Why? Exploitative data practices erode trust (e.g., Facebook’s $5B FTC fine).
• Trap: Moral Disengagement ("It’s Just Code")
• Prevention: Humanize impacts (e.g., Google’s "AI Principles" include "avoid creating or reinforcing unfair bias"). Why? Tech teams often distance themselves from harm (e.g., YouTube’s recommendation algorithm radicalizing users).
• Trap: Ethical Washing
• Prevention: Tie ethics to KPIs (e.g., Salesforce’s Chief Ethical Use Officer). Why? PR stunts backfire (e.g., Google’s canceled AI ethics board after backlash).

Legal & Compliance Notes

• GDPR (EU): Grants "right to explanation" for automated decisions (Art. 22) and fines up to 4% of global revenue for violations (e.g., Amazon’s €746M fine).
• Algorithmic Accountability Act (Proposed, US): Would require impact assessments for high-risk AI (e.g., hiring, lending).
• EU AI Act (2024): Bans social scoring and high-risk AI (e.g., predictive policing) with fines up to €30M or 6% of revenue.
• Section 230 (US): Shields platforms from liability for user content—but under fire for enabling harm (e.g., Facebook’s role in Myanmar genocide).
• BIPA (Illinois): Biometric data laws require consent (e.g., Facebook’s $650M settlement for facial recognition).

Quick Case Scenarios

1. Dilemma: Your company’s AI hiring tool favors candidates from elite universities, excluding qualified applicants from community colleges. What do you do?

2. Answer: Pause deployment and audit for bias (Justice as Fairness). Justification: Rawls’ "veil of ignorance" demands fairness for all candidates, not just privileged groups.

3. Dilemma: A social media platform’s algorithm maximizes engagement by promoting outrage and misinformation. Is this ethical?

4. Answer: No—redesign the algorithm to prioritize well-being (Care Ethics). Justification: Gilligan’s ethics of care values relationships over profit, even if engagement drops.

Last-Minute Cram Sheet

1. Utilitarianism: Greatest good for greatest number—but can justify harming minorities (e.g., predictive policing).
2. Deontology: Rules > outcomes—GDPR’s "right to explanation" is Kantian.
3. Virtue Ethics: Focus on character—e.g., Microsoft’s AI ethics board.
4. Justice as Fairness: Fairness requires impartiality—e.g., COMPAS recidivism bias.
5. Stakeholder Theory: Balance all interests—e.g., TikTok’s teen mental health crisis.
6. Algorithmic Neutrality Fallacy: "Code is neutral" is false—e.g., Apple Card’s gender bias.
7. Surveillance Capitalism: Exploiting data for profit—e.g., Facebook’s $5B FTC fine.
8. GDPR: EU law with "right to explanation" and 4% revenue fines.
9. EU AI Act: Bans high-risk AI like social scoring.
10. Key Cases: Amazon hiring bias, Volkswagen Dieselgate, Facebook Myanmar, COMPAS recidivism tool.