Bias in
artificial intelligence refers to the predispositions or systematic errors that can affect the decisions and outcomes of
AI models. These biases can arise from the
training data, the
algorithms used, or human decisions during the model's development.
Imagine bias as a "distorted lens" through which the
AI model views the world. If the data (
datasets) used to
train the model are biased, for example, if they contain more information about one group of people than another, the model may make unfair or inaccurate decisions.
Moreover, the
algorithms themselves can introduce biases if not carefully designed. This can lead to results that perpetuate or amplify existing inequalities. For example, a resume selection
algorithm might favor certain candidates based on historical hiring patterns, unfairly excluding others.
To mitigate bias, it is crucial to review and diversify the
training data, as well as regularly audit and adjust the
algorithms. Transparency and ethics in AI development are essential to minimize these issues and ensure that models are fair and equitable.