Algorithmic bias is when a computer program, such as an algorithm or AI system, makes decisions or predictions that unfairly favor or disadvantage certain groups of people. This usually occurs because the data used to train the algorithm contains historical or societal biases, leading the AI to perpetuate and sometimes amplify these biases in its decision-making.
For example, imagine you have a hiring algorithm that sorts through job applications. Suppose the data used to train this algorithm comes from a time when people from a particular demographic group were predominantly hired for certain roles. In that case, the algorithm might unintentionally favor applicants in those groups, even if equally qualified applicants from other demographic groups apply. This would be an instance of algorithmic bias.
To prevent algorithmic bias, it is essential to be aware of potential sources of unfairness in the data and to actively work towards reducing those biases. This involves a combination of diverse data sources, careful algorithm design, and continuous monitoring to ensure fairness and equity in AI-driven decision-making. By doing this, we can help create a more inclusive and just digital world.