There will be many ethical issues facing data science methods in the future, especially with regard to bias, privacy, and openness. Protecting sensitive data and getting user consent will be crucial as data collecting expands. Another danger is algorithmic bias, which can affect vulnerable communities by producing unfair or discriminatory results from models built on biassed data. Furthermore, users find it challenging to comprehend or contest data-driven decisions due to the opaque nature of complicated models, such as deep learning, which poses transparency problems. Regulation and responsible procedures are necessary to address these ethical concerns. A data science course might offer crucial instruction in ethical data handling for a deeper comprehension.
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