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

Machine learning plays a role in many deployed decision systems, often in ways that are
difficult or impossible to understand by human stakeholders. Explaining, in a human …

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

Strategic classification regards the problem of learning in settings where users can
strategically modify their features to improve outcomes. This setting applies broadly, and has …

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 …

Although machine learning (ML) algorithms are widely used to make decisions about
individuals in various domains, concerns have arisen that (1) these algorithms are …

Dynamic learning systems subject to selective labeling exhibit censoring, ie persistent
negative predictions assigned to one or more subgroups of points. In applications like …

The use of algorithmic decision making systems in domains which impact the financial,
social, and political well-being of people has created a demand for these to be “fair” under …

Real-world systems often involve some pool of users choosing between a set of services.
With the increase in popularity of online learning algorithms, these services can now self …

We initiate the study of strategic behavior in screening processes with multiple classifiers.
We focus on two contrasting settings: a" conjunctive” setting in which an individual must …