  Content creators compete for user attention. Their reach crucially depends on
algorithmic choices made by developers on online platforms. To maximize
exposure, many creators adapt strategically, as evidenced by examples like the
sprawling search engine optimization industry. This begets competition for the
finite user attention pool. We formalize these dynamics in what we call an
exposure game, a model of incentives induced by algorithms, including modern
factorization and (deep) two-tower architectures. We prove that seemingly
innocuous algorithmic choices, e.g., non-negative vs. unconstrained
factorization, significantly affect the existence and character of (Nash)
equilibria in exposure games. We proffer use of creator behavior models, like
exposure games, for an (ex-ante) pre-deployment audit. Such an audit can
identify misalignment between desirable and incentivized content, and thus
complement post-hoc measures like content filtering and moderation. To this
end, we propose tools for numerically finding equilibria in exposure games, and
illustrate results of an audit on the MovieLens and LastFM datasets. Among
else, we find that the strategically produced content exhibits strong
dependence between algorithmic exploration and content diversity, and between
model expressivity and bias towards gender-based user and creator groups.
