We develop a model of content filtering as a game between the filter and the content consumer, where the latter incurs information costs for examining the content. Motivating examples include censoring misinformation, spam/phish filtering, and recommender systems acting on a stream of content. When the attacker is exogenous, we show that improving the filter’s quality is weakly Pareto improving, but has no impact on equilibrium payoffs until the filter becomes sufficiently accurate. Further, if the filter does not internalize the consumer’s information costs, its lack of commitment power may render it useless and lead to inefficient outcomes. When the attacker is also strategic, improvements in filter quality may decrease equilibrium payoffs.

Inspections are frequently conducted to reveal information about agents' otherwise unobserved actions. Some inspections occur at pre-announced times; others are surprises. We show how the productive role of the inspected agent determines whether predictable or random inspections are optimal. If the agent's main task is achieving a breakthrough---think of an entrepreneur investing in an innovative industry---then predictable inspections are optimal. If the main task is avoiding a breakdown---think of a financial institution managing its risk in order to avoid default---then random inspections are optimal.