Algorithmic bias means the unfair results created by computer systems or AI. Even if machines look neutral, they learn from data created by humans. If the data has problems, the algorithm will repeat these problems and make them bigger. This is why algorithmic bias is an important topic today.
One common example is in job recruitment. Some AI hiring systems prefer people with certain names, genders, or backgrounds because the training data was not balanced. Another example is facial recognition. Research shows it is often less accurate for women and people with darker skin. These mistakes can cause real-world harm.
Algorithmic bias does not always happen on purpose. Many engineers do not notice that their data is incomplete or includes stereotypes. But even unintentional bias can still produce unfair outcomes.
To reduce bias, companies and developers need to check their datasets, include more diverse information, and run fairness tests. Users should also understand that AI is not perfect and must be used carefully. In a digital society, fairness should not be decided only by algorithms.
