Fairness

Fairness in data science refers to the absence of bias or discrimination in the data, algorithms, and models used in decision-making processes. It is important to ensure that the data used to train models is representative of the population it is intended to serve, and that the models do not perpetuate or amplify existing biases. Fairness can be achieved through various techniques such as data preprocessing, algorithmic adjustments, and model evaluation. Fairness is a crucial aspect of responsible AI and is essential for building trust in AI systems.
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