1. Introduction to AI and Ethics
Definition of AI:
- Artificial Intelligence (AI) refers to machines or systems that mimic human cognitive functions like learning, problem-solving, and decision-making.
- Ethics: Moral principles governing behavior, emphasizing fairness, accountability, and transparency.
Importance of AI Ethics:
- AI’s rapid integration into healthcare, finance, criminal justice, and daily life raises urgent ethical questions.
- Unaddressed ethical issues risk exacerbating inequality, eroding privacy, and undermining human autonomy.
2. Key Ethical Dilemmas in AI
2.1 Bias and Fairness
Causes of Bias:
- Training Data: Historical data reflecting societal prejudices (e.g., racial bias in facial recognition).
- Algorithmic Design: Flawed metrics prioritizing efficiency over equity.
Examples:
- COMPAS Algorithm: Higher risk scores for Black defendants in criminal sentencing.
- Hiring Tools: Gender bias in tech job recruitment.
Mitigation Strategies:
- Diversify training datasets and audit algorithms for fairness.
- Implement frameworks like IBM’s AI Fairness 360.
2.2 Privacy and Surveillance
Data Exploitation:
- Mass data collection by corporations/governments risks misuse (e.g., Cambridge Analytica scandal).
- Facial Recognition: Pervasive surveillance threatening civil liberties.
Consent Challenges:
- Users often unaware of data usage scope.
- Solution: Stricter regulations like GDPR (EU) and CCPA (California).
2.3 Accountability and Responsibility
The Responsibility Gap:
- Who is liable when AI errs? Developers, users, or the AI itself?
- Case Study: Tesla Autopilot accidents—blame assigned to drivers vs. software flaws.
Legal Frameworks:
- EU’s proposed AI Act mandates transparency and human oversight.
2.4 Job Displacement and Economic Inequality
Impact:
- Automation could displace 85 million jobs by 2025 (World Economic Forum).
- Sectors at risk: Manufacturing, retail, transportation.
Solutions:
- Reskilling programs and universal basic income (UBI) proposals.
2.5 Autonomous Weapons and Warfare
Risks:
- Lethal AI systems could bypass human judgment, escalating conflicts.
- Example: AI-driven drones targeting without ethical deliberation.
Regulation Efforts:
- Campaigns to ban autonomous weapons, akin to chemical weapons treaties.
2.6 Transparency and Explainability
Black Box Problem:
- Complex AI models (e.g., neural networks) lack interpretability.
Explainable AI (XAI):
- Tools like LIME (Local Interpretable Model-agnostic Explanations) clarify decision-making processes.
2.7 Environmental Impact
Energy Consumption:
- Training GPT-3 emits 552 tons of CO₂—equivalent to 120 cars annually.
- Sustainable AI: Optimizing algorithms for energy efficiency.
2.8 AI in Healthcare
Ethical Challenges:
- Diagnostic errors due to biased data (e.g., underrepresentation of minorities).
- Case Study: IBM Watson’s inaccurate cancer treatment recommendations.
Opportunities:
- Early disease detection and personalized medicine.
2.9 Social Manipulation and Democracy
Algorithmic Influence:
- Social media algorithms amplifying misinformation (e.g., 2016 U.S. elections).
- Deepfakes: Undermining trust in media and institutions.
Countermeasures:
- Detection tools and digital literacy campaigns.
2.10 Global Governance and Regulation
Divergent Approaches:
- EU: Risk-based regulation via the AI Act.
- U.S.: Sector-specific guidelines favoring innovation.
- China: State-controlled AI development for surveillance.
Need for Collaboration:
- International bodies like UN must harmonize standards to prevent a regulatory race to the bottom.
3. Case Studies
3.1 COMPAS in Criminal Justice
- Algorithmic bias led to longer sentences for Black defendants, questioning fairness in predictive policing.
3.2 Tesla Autopilot Accidents
- Highlighted accountability gaps in semi-autonomous systems.
3.3 Facebook-Cambridge Analytica
- Data misuse influencing elections, underscoring privacy and manipulation risks.
4. Conclusion
- Summary: AI’s ethical dilemmas span bias, privacy, accountability, and beyond, requiring multidisciplinary solutions.
- Call to Action: Developers, policymakers, and civil society must collaborate to embed ethics into AI design and governance.
5. References
- European Commission. (2021). Proposal for a Regulation on Artificial Intelligence.
- IEEE. (2019). Ethically Aligned Design: A Vision for Prioritizing Human Well-being.
- O’Neil, C. (2016). Weapons of Math Destruction. Broadway Books.
- World Economic Forum. (2020). The Future of Jobs Report.
Exam Preparation Tips:
- Key Terms: Algorithmic bias, explainable AI, moral agency, GDPR.
- Essay Questions:
- “Can AI ever be truly unbiased? Discuss with examples.”
- “Who should be held accountable for AI-induced harm? Justify your answer.”
This module equips students to critically analyze AI’s ethical challenges, a vital skill for exams and real-world applications.