Why AI Ethics Matters
AI systems are making decisions that affect people's lives—who gets a loan, who gets hired, what content you see, even in some cases who gets parole. When AI gets it wrong, real people are harmed.
Core Question
AI is a tool. The ethical questions are: Who builds it? With what values? Who benefits? Who could be harmed?
Key Ethical Issues
Bias and Discrimination
AI learns from data—if that data reflects historical biases, the AI will too.
- Hiring algorithms that discriminate against women (learned from male-dominated hiring history)
- Facial recognition that works worse on darker skin tones
- Loan algorithms that reflect historical redlining patterns
Bias isn't just a technical problem—it requires examining what data we use and what outcomes we optimize for.
Privacy
AI often requires massive data collection:
- LLMs trained on personal data scraped from the internet
- Facial recognition surveillance in public spaces
- Predictive systems that infer sensitive information
Questions: Should AI companies use your data without asking? Can you opt out? Who owns insights derived from your data?
Misinformation & Deepfakes
AI can generate convincing fake content:
- Deepfake videos of politicians saying things they never said
- AI-generated fake news articles
- Cloned voices used for scams
This threatens trust in authentic media and enables new forms of fraud and manipulation.
Job Displacement
AI automation is changing the job market:
- Some jobs will be eliminated
- Others will be transformed
- New jobs will emerge
The ethical question isn't just "can we automate this?" but "should we?" and "how do we support affected workers?"
Autonomy and Decision-Making
Should AI make high-stakes decisions?
- Self-driving cars deciding in crash scenarios
- AI in healthcare making diagnoses
- Autonomous weapons systems
Humans need to maintain meaningful control over consequential decisions.
AI Safety
Beyond ethics, there are safety concerns about AI systems:
- Alignment — Making sure AI does what we actually want
- Robustness — AI that doesn't fail in unexpected ways
- Controllability — Ability to correct or shut down AI systems
Responsible AI Use
As an AI user, you can:
- Verify AI outputs before trusting them
- Consider who might be affected by AI-assisted decisions
- Be transparent when using AI to create content
- Support regulation and accountability measures
Summary
- • AI reflects the biases in its training data
- • Privacy, misinformation, and job displacement are key concerns
- • Humans should maintain control over high-stakes decisions
- • Responsible use means verifying, questioning, and being transparent