Preview
IB DP Computer Science — A4 Machine learning — Ethical considerations | SL | A4.4.1
📋 What You Will Learn
- Discuss the nine key ethical issues: accountability, algorithmic fairness, bias, consent, environmental impact, privacy, security, societal impact, transparency
- Explain how biases enter training data and why they are hard to remove
- Describe how biased models create feedback loops that reinforce unfairness
- Discuss the ethics of ML in online communication: misinformation, bias, harassment, anonymity, privacy
- Construct balanced Discuss -style answers that weigh benefits against harms and reach a judgement
⚖️ Why This Matters A model that decides who gets a loan, a job interview or bail is not a neutral calculator — it is yesterday's human decisions, with all their prejudices, compressed into mathematics and executed at scale. The ethics question is not "is the model accurate?" but "accurate at reproducing what , for