Discrimination

In Data Science, discrimination refers to the unfair or biased treatment of individuals or groups based on their membership in a particular demographic category, such as race, gender, age, or religion. Discrimination can occur in various stages of the data science pipeline, including data collection, preprocessing, modeling, and deployment. For example, biased data collection methods or incomplete datasets can lead to discriminatory outcomes in machine learning models. Similarly, biased algorithms or inappropriate performance metrics can perpetuate or amplify existing biases. Discrimination in data science can have serious consequences, such as perpetuating social inequalities, reinforcing stereotypes, and violating ethical and legal principles.
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