We select the median-difficulty question from the set with maximum model coverage and standardize it to 0.

Finally, from a practical point of view, we suggest the adoption of "privacy label," food-like notices, that provide the required information in an easily understandable manner, making the privacy policies easier to read. Through standard symbols, colors and feedbacks — including yes/no statements, where applicable — critical and specific scenarios are identified. For example, whether or not the organization actually shares the information, under what specific circumstances this occurs, and whether individuals can oppose the share of their personal data. This would allow some kind of standardized information. Some of the key points could include the information collected and the purposes of its collection, such as marketing, international transfers or profiling, contact details of the data controller, and distinct differences between organizations’ privacy practices, and to identify privacy-invasive practices.

many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the l1 and l2 regularizers of linear models) assume that all features are centered around zero and have variance in the same order. If a feature has a variance that is orders of magnitude larger than others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.