Lessons·life-skills·adults · intermediate
The Ethics of AI and Technology
Explore the ethical questions surrounding artificial intelligence — from algorithmic bias and job displacement to deepfakes and surveillance — and develop your own informed position on where the line should be drawn.
About 30 minutes
An AI system reviews 500 CVs for a job. It consistently ranks men higher than women. Nobody programmed it to be sexist. So whose fault is it?
Before you start
Watch Can We Build AI Without Losing Control Over It? by TED (Sam Harris) (14 min) — Focus on Sam Harris's argument about why we should be concerned about superintelligence. Do you find his reasoning convincing, or do you think the risks are overstated?
- How many AI-powered tools do you use daily? (Think beyond obvious ones — autocorrect, recommendation algorithms, GPS routing, spam filters all count.)
- Have you ever felt that an algorithm 'knew you' in an uncomfortable way? What happened?
- If a self-driving car must choose between hitting one person or another, who should make that decision — the programmer, the car owner, or the government?
The Rise of the Machines (For Real This Time)
In 2014, Amazon built an AI recruiting tool to screen CVs automatically. By 2015, they realised it was penalising candidates who included the word 'women's' (as in 'women's chess club captain') and downgrading graduates of all-women's colleges. The AI had learned from a decade of Amazon's hiring data — which reflected the tech industry's existing gender bias. The system was scrapped in 2017.
This wasn't a bug. The AI worked exactly as designed — it found patterns in historical data and replicated them. The problem was that historical data contained historical bias. This is the fundamental challenge of AI ethics: algorithms don't have values. They optimise for whatever target we give them, using whatever data we feed them.
In 2018, a study found that a healthcare algorithm used across the United States to allocate medical care to patients was systematically discriminating against Black patients. It used healthcare spending as a proxy for health needs — but because Black patients historically had less access to healthcare and therefore lower spending, the algorithm concluded they were healthier and needed less care. An estimated 46 million decisions were affected.
Meanwhile, deepfake technology has made it possible to create convincing fake videos of anyone saying anything. In 2023, a finance worker in Hong Kong transferred $25 million after a video call with what appeared to be his company's CFO — it was entirely AI-generated. Facial recognition systems have been deployed by police forces despite error rates of up to 81% for dark-skinned women.
AI is not science fiction. It is making decisions about your loan applications, your social media feed, your job prospects, and potentially your medical treatment — right now.
Five Ethical Lenses on AI
**1. The Bias Lens — Is it fair?**
AI learns from data, and data reflects society's existing biases. If you train a system on biased data, you get biased outputs — at scale. Questions: Whose data was used? Who was left out? Does the system produce equal outcomes across demographics?
**2. The Transparency Lens — Can we understand it?**
Many AI systems are 'black boxes' — they produce outputs, but nobody (including their creators) can fully explain how. If an AI denies your mortgage application, do you have the right to know why? The EU's GDPR says yes. But the AI might not be able to explain itself.
**3. The Autonomy Lens — Who decides?**
When AI makes recommendations about criminal sentencing, medical diagnosis, or hiring, who bears responsibility? The programmer? The company? The user who accepts the recommendation? If nobody is responsible, that's a problem.
**4. The Labour Lens — What about jobs?**
McKinsey estimates that 800 million jobs globally could be displaced by automation by 2030. But technology has always destroyed and created jobs — the printing press put scribes out of work but created publishers, librarians, and journalists. The question isn't whether jobs will change, but whether we'll support people through the transition.
**5. The Power Lens — Who benefits?**
AI development is concentrated in a handful of companies — primarily in the US and China. These companies hold unprecedented power over information, commerce, and communication. Is it healthy for society when a few corporations control the most transformative technology since electricity?
Why This Matters to You
You're already living in an AI-shaped world. Your social media feeds are curated by algorithms that prioritise engagement (not truth or wellbeing). Your credit score may be influenced by AI. If you've applied for a job at a large company recently, an AI may have screened your CV before a human ever saw it.
The decisions being made now — by governments, companies, and developers — will shape the next fifty years. The EU's AI Act (2024) is the first major attempt to regulate AI by risk level. China requires AI-generated content to be labelled. The UK is taking a lighter-touch, sector-specific approach.
You don't need to be a programmer to have an informed opinion on AI ethics. You need to understand the trade-offs: convenience vs privacy, efficiency vs employment, innovation vs safety. These aren't technical questions — they're human questions. And they need human answers.
Key Concepts
- Algorithmic Bias
- When an AI system produces systematically unfair outcomes due to biases in training data or design choices.A facial recognition system trained mainly on light-skinned faces performs poorly on dark-skinned faces — not from malice, but from data gaps.
- Black Box Problem
- When an AI system's decision-making process is opaque — it produces outputs but cannot explain its reasoning.A neural network denies a loan application. When asked why, the answer is essentially 'the math said so' — no human-understandable reason can be given.
- Deepfake
- AI-generated synthetic media — video, audio, or images — that realistically depicts someone saying or doing things they never did.In 2024, deepfake robocalls impersonating President Biden told voters not to vote in the New Hampshire primary.
- The Alignment Problem
- The challenge of ensuring AI systems pursue goals that match human values and intentions.If you tell an AI to 'maximise user engagement', it might learn that outrage generates the most clicks — technically achieving its goal while making users miserable.
- Automation Displacement
- Job losses caused by technology performing tasks previously done by humans.Self-checkout machines in supermarkets reduced cashier jobs. AI is now doing the same to data entry, basic legal research, and customer service roles.
- Explainable AI (XAI)
- AI systems designed to provide human-understandable explanations for their decisions.Instead of just saying 'loan denied', an explainable system would say 'loan denied because debt-to-income ratio exceeds threshold and employment history is under 2 years.'
Let's Discuss
- How many AI-powered tools do you use daily? Were you aware of all of them before this lesson?
- If an AI system makes a wrong medical diagnosis that harms a patient, who should be held legally responsible — the hospital, the AI developer, or nobody?
- Should governments ban deepfakes entirely, or is that an unacceptable restriction on free expression? Where's the line?
- Devil's advocate: AI bias simply reflects human bias that already exists. If a biased AI replaces a biased human recruiter, and the AI is slightly less biased, isn't that still an improvement? Should we let 'perfect' be the enemy of 'better'?
Technology Ethics Vocabulary
| English | Türkçe |
|---|---|
| algorithmSocial media algorithms decide what you see — they optimise for engagement, not for what's true or healthy. | algoritmaSosyal medya algoritmaları ne göreceğinize karar verir; neyin doğru veya sağlıklı olduğuna değil, etkileşimi artırmaya odaklanırlar. |
| surveillanceChina's social credit system uses AI surveillance to monitor and score citizens' behaviour. | gözetleme / izlemeÇin'in sosyal kredi sistemi, vatandaşların davranışlarını izlemek ve puanlamak için yapay zeka gözetimini kullanıyor. |
| consentDid you give informed consent when you accepted those terms and conditions? Most people don't read them. | onay / rızaO kullanım koşullarını kabul ederken bilgilendirilmiş rıza verdiniz mi? Çoğu insan bunları okumuyor. |
| accountabilityThe accountability gap in AI means that when things go wrong, it's often unclear who's responsible. | hesap verebilirlik / sorumlulukYapay zekadaki hesap verebilirlik açığı, işler ters gittiğinde sorumlunun kim olduğunun genellikle belirsiz olması anlamına geliyor. |
| disruptiveAI is a disruptive technology — it's not just improving existing systems, it's fundamentally changing how they work. |
Writing task
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