- Define and explain Bias and Fairness in your own words
- Use key terms such as bias accurately
- Apply what you have learned to new examples and questions
- Avoid the common mistakes learners make with this topic
This lesson focuses on Bias and Fairness: explore how biased data creates unfair AI — and how to fight it.
Explore how biased data creates unfair AI — and how to fight it.
Key ideas
Biased data in, biased decisions out
A hiring tool trained on a decade of a company's past hires will learn to prefer the kind of people the company used to hire — baking old discrimination into new software. Because the bias hides inside millions of tuned numbers, it is hard to spot without deliberately testing for fairness across groups.
Learning replaces hand-written rules
Nobody could write rules to recognise every cat photo, so instead a model is shown thousands of labelled examples and adjusts its internal numbers until it predicts well. This is training; the model's skill is then tested on new examples it has never seen. If it only memorises the training set — overfitting — it fails on anything new.
Key term — bias: A systematic unfairness in a model's decisions, usually inherited from unrepresentative or prejudiced training data.
A university uses AI to screen applications, trained on ten years of past admissions. Name one fairness risk.
The model may learn to prefer applicants resembling past successful ones, entrenching old biases against under-represented groups.
Answer: The model may learn to prefer applicants resembling past successful ones, entrenching old biases against under-represented groups.
- Assuming AI is neutral because maths is neutral Correction: models inherit the biases of their training data — neutrality must be tested for, never assumed.
- Trusting a high accuracy score without asking what it was tested on Correction: always ask whether the test data was balanced and representative — 98% on easy data can hide total failure elsewhere.
Practice
False — remaining details like postcode or school can act as proxies for protected characteristics, so bias can survive.
In traditional programming humans write explicit rules; in machine learning the program finds its own patterns by training on data.
Its training data contains no night-time patterns, so it never learned the features of dark images — it can only recognise what it has seen.
Overfitting is when a model memorises training examples instead of learning general patterns; it shows as high training accuracy but poor accuracy on unseen test data.
Quick check
Which of these best defines "bias"?
You use an AI chatbot to draft your history essay. What is the responsible way to handle this?
- Bias and Fairness: explore how biased data creates unfair AI — and how to fight it.
- Biased data in, biased decisions out: A hiring tool trained on a decade of a company's past hires will learn to prefer the kind of people the company used to hire — baking old discrimination into new software.
- machine learning: A branch of AI where programs improve at a task by finding patterns in data, rather than by following explicitly programmed rules.
- Watch out for: assuming AI is neutral because maths is neutral