  We consider situations where a user feeds her attributes to a machine
learning method that tries to predict her best option based on a random sample
of other users. The predictor is incentive-compatible if the user has no
incentive to misreport her covariates. Focusing on the popular Lasso estimation
technique, we borrow tools from high-dimensional statistics to characterize
sufficient conditions that ensure that Lasso is incentive compatible in large
samples. In particular, we show that incentive compatibility is achieved if the
tuning parameter is kept above some threshold. We present simulations that
illustrate how this can be done in practice.
