Manipulation is a concern in many domains, such as social media, advertising, and
chatbots. As AI systems mediate more of our digital interactions, it is important to understand …

We introduce the notion of performative power, which measures the ability of a firm
operating an algorithmic system, such as a digital content recommendation platform, to …

Predictions about people, such as their expected educational achievement or their credit
risk, can be performative and shape the outcome that they are designed to predict …

Algorithmic recommender systems such as Spotify and Netflix affect not only consumer
behavior but also producer incentives. Producers seek to create content that will be shown …

As predictive models are deployed into the real world, they must increasingly contend with
strategic behavior. A growing body of work on strategic classification treats this problem as a …

Content creators compete for user attention. Their reach crucially depends on algorithmic
choices made by developers on online platforms. To maximize exposure, many creators …

In performative prediction, the deployment of a predictive model triggers a shift in the data
distribution. As these shifts are typically unknown ahead of time, the learner needs to deploy …

We initiate the study of the effects of non-transparency in decision rules on individuals' ability
to improve in strategic learning settings. Inspired by real-life settings, such as loan approvals …

Strategic classification studies learning in settings where self-interested users can
strategically modify their features to obtain favorable predictive outcomes. A key working …

Predictions in the social world generally influence the target of prediction, a phenomenon
known as performativity. Self-fulfilling and self-negating predictions are examples of …