  A central concern in an interactive intelligent system is optimization of its
actions, to be maximally helpful to its human user. In recommender systems for
instance, the action is to choose what to recommend, and the optimization task
is to recommend items the user prefers. The optimization is done based on
earlier user's feedback (e.g. "likes" and "dislikes"), and the algorithms
assume the feedback to be faithful. That is, when the user clicks "like," they
actually prefer the item. We argue that this fundamental assumption can be
extensively violated by human users, who are not passive feedback sources.
Instead, they are in control, actively steering the system towards their goal.
To verify this hypothesis, that humans steer and are able to improve
performance by steering, we designed a function optimization task where a human
and an optimization algorithm collaborate to find the maximum of a
1-dimensional function. At each iteration, the optimization algorithm queries
the user for the value of a hidden function $f$ at a point $x$, and the user,
who sees the hidden function, provides an answer about $f(x)$. Our study on 21
participants shows that users who understand how the optimization works,
strategically provide biased answers (answers not equal to $f(x)$), which
results in the algorithm finding the optimum significantly faster. Our work
highlights that next-generation intelligent systems will need user models
capable of helping users who steer systems to pursue their goals.
