What is algorithmic bias in AI systems?

Algorithmic bias in AI systems refers to the tendency of AI algorithms to produce results that are systematically prejudiced due to erroneous assumptions in the machine learning process. When AI systems are trained on skewed datasets, they can inadvertently learn and perpetuate those biases present in the data. This means that if the training data lacks diversity or is unrepresentative of the wider population, the AI might make decisions that favor certain groups over others, which leads to unfair treatment or discrimination in outcomes.

For instance, if an AI model is trained predominantly on data from a particular demographic, it may not perform accurately or fairly for individuals outside that group. This highlights the crucial importance of understanding and addressing the quality and diversity of the data used to train AI systems to mitigate bias and promote equity in their applications.

The other options do not accurately capture the nature of algorithmic bias. The assertion that AI systems always make fair decisions ignores the potential pitfalls of bias in the underlying data. Stating that AI systems cannot change their algorithms disregards the fact that they can be adjusted, although changes do not always correct previous biases. Finally, claiming that algorithm updates always correct biases oversimplifies the complex nature of bias in AI, as not all updates are guaranteed to address or eliminate