  Algorithms engineered to leverage rich behavioral and biometric data to
predict individual attributes and actions continue to permeate public and
private life. A fundamental risk may emerge from misconceptions about the
sensitivity of such data, as well as the agency of individuals to protect their
privacy when fine-grained (and possibly involuntary) behavior is tracked. In
this work, we examine how individuals adjust their behavior when incentivized
to avoid the algorithmic prediction of their intent. We present results from a
virtual reality task in which gaze, movement, and other physiological signals
are tracked. Participants are asked to decide which card to select without an
algorithmic adversary anticipating their choice. We find that while
participants use a variety of strategies, data collected remains highly
predictive of choice (80% accuracy). Additionally, a significant portion of
participants became more predictable despite efforts to obfuscate, possibly
indicating mistaken priors about the dynamics of algorithmic prediction.
