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.

翻译：内容创作者争夺用户注意力，其触达范围关键取决于在线平台开发者的算法选择。为最大化曝光度，许多创作者会进行策略性调整，正如蓬勃发展的搜索引擎优化行业所证实的案例。这引发了对有限用户注意力池的竞争。我们将在所谓的曝光博弈中形式化这些动态——这是一种由算法（包括现代因子分解和（深度）双塔架构）引发的激励模型。我们证明了看似无害的算法选择（如非负因子分解与无约束因子分解）会显著影响曝光博弈中（纳什）均衡的存在性及其特征。我们主张将曝光博弈等创作者行为模型用于（事前）部署前审计。此类审计可识别理想内容与被激励内容之间的错位，从而补充事后措施（如内容过滤与审核）。为此，我们提出了数值求解曝光博弈均衡的工具，并在MovieLens和LastFM数据集上展示了审计结果。我们发现，策略性生成的内容表现出算法探索与内容多样性之间的强相关性，以及模型表达力与基于性别的用户和创作者群体偏见之间的强相关性。