Should Humans Lie to Machines?  The Incentive Compatibility of Lasso and General Weighted Lasso

many online platforms try to predict which content - a song, a video, a post, or an article - is the best fit for each user. Medical providers have also begun using machine learning techniques to automate check-ups and test appointments for patients based on their medical history. Typically, these automated systems use data from past users to estimate a model that relates the best fit for a user (such as the most preferred content or the appropriate medical test) to her characteristics. These estimates are then applied to a new user’s characteristics, which she discloses either actively or passively via her past online behavior (which may be reflected in her cookies or collected by her browser). Given the growing interaction of users with such automated systems, it is only natural to ask whether a user should truthfully disclose her characteristics?

If the information the user discloses is also used to exploit her (say, by providing it to third parties for advertising or price discrimination), then the user has an obvious reason not to reveal her private information. The question is whether special features of some popular machine learning methods introduce an incentive to misreport one’s personal characteristics even when this information will be used solely for predicting her best outcome?1 This question is of crucial importance: If individuals submit false reports to systems that rely on these reports for estimation and predictions, then the conclusions drawn from such estimates and predictions will be wrong and may lead to quite undesirable outcomes (e.g., think of an automated medical platform that schedules tests for patients based on false reports on attributes such as smoking, drinking and physical exercise).