AI Ethics and Concerns

AI is powerful, but power without responsibility is dangerous. In this lesson, we'll explore the ethical issues surrounding AI — the concerns, the debates, and what you should know as an informed citizen.

What You'll Learn

By the end of this lesson, you'll understand the major ethical concerns around AI and be able to think critically about AI's role in society.

Why Ethics Matters

AI systems make decisions that affect people's lives:

• Who gets a loan
• Who gets hired
• What content you see
• What medical treatment is recommended
• Who gets stopped by police

When AI makes mistakes or embeds biases, real people are harmed. Understanding ethics helps you:

• Use AI responsibly
• Advocate for better AI
• Recognize when AI systems are problematic

Bias and Fairness

The Problem

AI learns from data. If the data reflects historical biases, the AI will too.

Examples:

AI System	Bias Found
Hiring AI	Favored male candidates (trained on historically male-dominated hires)
Facial recognition	Higher error rates for darker-skinned faces
Healthcare AI	Underestimated illness severity for Black patients
Loan approval AI	Discriminated against certain neighborhoods
Language models	Reproduced gender stereotypes

Why It Happens

• Training data reflects history: Past discrimination is encoded in historical data
• Underrepresentation: Some groups have less training data
• Proxy variables: AI finds patterns that correlate with protected characteristics
• Developer blind spots: Teams may not anticipate all use cases

What's Being Done

• Bias testing: Evaluating AI across demographic groups
• Diverse teams: Including varied perspectives in AI development
• Fairness constraints: Building fairness into AI objectives
• Regulation: Laws requiring bias audits in high-stakes AI
• Transparency: Making AI decisions explainable

What You Can Do

• Be skeptical of AI decisions in important contexts
• Ask whether AI systems have been tested for fairness
• Support diverse representation in tech
• Report apparent bias when you encounter it

Privacy and Surveillance

Data Collection

AI needs data — often your data:

• Personal information: Name, location, purchases
• Behavioral data: Browsing history, app usage, clicks
• Biometric data: Face, voice, fingerprints
• Communication: Emails, messages, calls (in some cases)

The Surveillance Concern

AI enables surveillance at unprecedented scale:

Capability	Concern
Facial recognition	Mass identification without consent
Social media analysis	Predicting behavior and beliefs
Location tracking	Detailed movement histories
Predictive policing	Pre-crime assumptions about individuals
Social credit systems	Rating citizens based on behavior

Privacy Trade-offs

The personalization paradox:

• More data → Better AI experiences
• More data → Greater privacy risks

There's no perfect answer, but you can:

• Understand what data you're sharing
• Adjust privacy settings
• Use privacy-focused alternatives when important
• Support privacy legislation

Job Displacement

The Fear

AI will automate jobs. This is real — but the picture is nuanced.

What's Likely to Happen

Impact	Explanation
Some jobs disappear	Tasks that are routine and predictable
Some jobs transform	AI handles parts, humans handle others
Some jobs emerge	New roles we can't fully predict
Transition pain	Real suffering during adjustment periods

Jobs Most at Risk

• Routine data processing
• Basic customer service
• Simple content creation
• Predictable physical tasks (with robotics)

Jobs More Protected (For Now)

• Complex reasoning and strategy
• Creative and novel work
• Emotional and social intelligence
• Physical work in unpredictable environments
• Expertise that requires accountability

What We Should Do

• Reskilling programs: Help workers transition
• Education reform: Prepare for AI-augmented work
• Social safety nets: Support during transitions
• Thoughtful deployment: Consider human impact of automation

What You Can Do

• Develop skills AI augments rather than replaces
• Stay adaptable and learning
• Advocate for responsible automation
• Consider impact when choosing to use AI

Misinformation and Manipulation

The Challenge

AI can create convincing fake content at scale:

• Deepfakes: Fake videos of real people
• Synthetic text: Automated fake news, reviews, comments
• Voice cloning: Fake audio messages
• Fake images: Never-happened events

Why It Matters

• Trust erosion: When anything can be faked, nothing is trusted
• Political manipulation: Fake content influencing elections
• Scams: Personalized, convincing fraud
• Harassment: Fake intimate images, defamation

What's Being Done

• Detection tools: AI to identify AI-generated content
• Watermarking: Invisible markers in AI content
• Content authentication: Cryptographic proof of authenticity
• Platform policies: Rules against synthetic media
• Media literacy: Teaching critical evaluation

What You Can Do

• Verify before sharing
• Check sources on important claims
• Be skeptical of emotionally charged content
• Use reverse image search
• Wait before believing breaking news

Accountability and Transparency

The Black Box Problem

Many AI systems are opaque:

• We don't know exactly how they make decisions
• Even creators can't fully explain outputs
• This makes errors hard to identify and fix

Why Transparency Matters

When AI denies you a loan, a job, or makes a medical recommendation, you deserve to know:

• What factors were considered?
• Why was this decision made?
• Is there a way to appeal?

The Accountability Gap

Who's responsible when AI causes harm?

• The developer?
• The company deploying it?
• The user?
• No one?

This is still being worked out legally and ethically.

What's Being Done

• Explainable AI (XAI): Making AI decisions interpretable
• Regulation: Laws requiring explanation rights
• Auditing: Third-party review of AI systems
• Documentation: Clear disclosure of AI use

Environmental Impact

AI's Carbon Footprint

Training large AI models uses significant energy:

Aspect	Impact
Training GPT-4 class models	Estimated hundreds of tons of CO2
Data centers	Significant electricity use
Water cooling	Water consumption for cooling
Hardware production	Environmental cost of chips

The Trade-off

AI can also help the environment:

• Optimizing energy grids
• Climate modeling
• Reducing waste in manufacturing
• Enabling remote work

What's Being Done

• More efficient AI architectures
• Renewable energy for data centers
• Research into "green AI"

Existential Risk

The Debate

Some researchers worry about advanced AI posing existential risks:

Arguments for concern:

• If AI becomes much smarter than humans, we may lose control
• Misaligned AI goals could have catastrophic consequences
• We should prepare before capabilities arrive

Arguments against emphasis:

• We're far from such capabilities
• Current AI has obvious limitations
• Focus should be on present harms

The Reasonable Position

Whether or not existential risk is near:

• It's worth researching AI safety
• We should be thoughtful about AI development
• Current real harms also deserve attention

Ethical AI Use: A Personal Framework

Questions to Ask

When using AI:

1. Accuracy: Can I verify this information?
2. Attribution: Should I disclose AI use?
3. Appropriateness: Is AI suitable for this context?
4. Impact: Who might be affected by this output?
5. Privacy: What data am I sharing?

Guidelines

• Don't deceive: Be honest about AI use when it matters
• Verify important information: Don't blindly trust AI
• Consider downstream effects: Your AI use affects others
• Respect policies: Follow workplace, school, and platform rules
• Stay informed: Ethics evolve as technology evolves

Key Takeaways

• Bias in AI reflects and can amplify historical discrimination
• Privacy trade-offs are real — understand what you're sharing
• Job displacement is genuine but nuanced — adaptation matters
• Misinformation from AI requires vigilance and verification
• Transparency and accountability are ongoing challenges
• Environmental impact of AI is significant but improving
• Ethical use is a personal responsibility — ask good questions

What's Next

Having explored where AI is now and its ethical dimensions, let's look forward. In the next lesson, we'll explore where AI is headed.