Classification with Strategically Withheld Data

Understand

Machine learning techniques can be useful in applications such as credit approval and college admission.

• However, to be classified more favorably in such contexts, an agent may decide to strategically withhold some of her features, such as bad test scores.
• This is a missing data problem with a twist: which data is missing {\em depends on the chosen classifier}, because the specific classifier is what may create the incentive to withhold certain feature values.
• We address the problem of training classifiers that are robust to this behavior.