Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …

We review and reflect on fairness notions proposed in machine learning literature and make
an attempt to draw connections to arguments in moral and political philosophy, especially …

Classification models are often used to make decisions that affect humans: whether to
approve a loan application, extend a job offer, or provide insurance. In such applications …

Growing AI readership (proxied for by machine downloads and ownership by AI-equipped
investors) motivates firms to prepare filings friendlier to machine processing and to mitigate …

Machine learning is increasingly used to inform decision-making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …

Consequential decision-making typically incentivizes individuals to behave strategically,
tailoring their behavior to the specifics of the decision rule. A long line of work has therefore …

We study the hidden-action principal-agent problem in an online setting. In each round, the
principal posts a contract that specifies the payment to the agent based on each outcome …

In this paper, we introduce a generalization of the standard Stackelberg Games (SGs)
framework: Calibrated Stackelberg Games. In CSGs, a principal repeatedly interacts with an …

When consequential decisions are informed by algorithmic input, individuals may feel
compelled to alter their behavior in order to gain a system's approval. Models of agent …

In performative prediction, predictions guide decision-making and hence can influence the
distribution of future data. To date, work on performative prediction has focused on finding …