We often think of computers as completely objective and fair. However, because AI systems learn from data created by humans, they can inherit our biases, leading to automated systems making unfair or discriminatory decisions.
Where Does Bias Come From?
AI models do not look at the real world; they look at 'training datasets.' If a model designed to screen resumes is trained on historical data from a company that only hired male engineers, the model learns that being male is a key feature of a successful engineer.
This is called data bias. The algorithm is not mean; it is just a mirror reflecting the historical inequalities present in its training data.
Identifying and Reducing Bias
Correcting bias requires auditing data. Engineers must check if datasets represent diverse groups fairly and tweak algorithms to prevent them from using protected attributes (like gender or race) to make decisions.