Based on four interviews, the paper argues that requirements engineering for ML needs new requirement types, including explainability, freedom from discrimination, data requirements, and an understanding of ML performance measures.
An act ive learning approach for improving the accuracy of automated domain mod el extraction,
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Requirements Engineering for Machine Learning: Perspectives from Data Scientists
Based on four interviews, the paper argues that requirements engineering for ML needs new requirement types, including explainability, freedom from discrimination, data requirements, and an understanding of ML performance measures.