Quest 1 • Lesson 7
⚖️ Bias & Fairness in AI
AI systems learn from data, and if that data reflects historical inequalities or prejudices, the AI will replicate – and even amplify – those biases. Building fair AI is not just a technical challenge but an ethical and societal one.
🌍 Real‑World AI Bias
A recidivism prediction tool used in US courts. It was found to be twice as likely to falsely flag Black defendants as higher risk compared to white defendants.
Many facial recognition systems have higher error rates for darker‑skinned faces and women, leading to misidentification and discrimination.
An Amazon recruiting AI showed bias against women because it was trained on resumes from a predominantly male workforce.
Some credit scoring models use variables that correlate with race or gender, perpetuating historical lending disparities.
🔍 How Bias Enters the AI Pipeline
- Data bias – training data under‑represents certain groups or reflects historical discrimination.
- Algorithmic bias – the model design (e.g., objective function) may unintentionally amplify disparities.
- Human bias – developers' choices (features, labels, thresholds) can introduce bias.
- Feedback loops – biased outputs influence future data, reinforcing the bias.
💭 Scenario: Building a Fair Hiring Tool
You're building an AI tool to screen job applicants. Your dataset contains resumes from the past 10 years. The data shows that 80% of successful hires were male, and many resumes include gender‑coded language (e.g., "competitive", "aggressive" vs "collaborative").
Question: Which approach would be most fair?
🛠️ Strategies to Mitigate Bias
- Diverse and representative data – collect data that reflects the full population.
- Bias audits – regularly test models for disparate impact across groups.
- Fairness metrics – use statistical measures (e.g., demographic parity, equal opportunity) to evaluate models.
- Explainability – ensure decisions can be understood and challenged.
- Inclusive teams – diverse development teams are more likely to identify potential biases.
- Continuous monitoring – bias can emerge over time; monitor models post‑deployment.
🧠 Quick Quiz
Which of the following is a fairness metric that ensures similar outcomes across groups?
✨ Challenge: Identify the Bias
Read the following scenario and identify the type of bias (data, algorithmic, or human).
Data bias – the training data is not representative of the population the model is applied to. This is a classic case of sampling bias.
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