Ethics and compliance are areas of interest to many in the data community. Beyond privacy, data professionals are much more engaged in topics such as fairness, accountability, transparency, and explainability in machine learning. Are data sets that are being used for model training representative of the broader population? For certain application domains and settings, transparency and interpretability are essential and regulators may require more transparent models, even at the expense of power and accuracy. More generally, how do companies mitigate risk when using ML?
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