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

Algorithmic decision-making in high-stakes domains often involves assigning decisions to
agents with incentives to strategically modify their input to the algorithm. In addition to …

A primary goal in strategic classification is to learn decision rules which are robust to
strategic input manipulation. Earlier works assume that strategic responses are known; while …

When users can benefit from certain predictive outcomes, they may be prone to act to
achieve those outcome, eg, by strategically modifying their features. The goal in strategic …

The primary goal in recommendation is to suggest relevant content to users, but optimizing
for accuracy often results in recommendations that lack diversity. To remedy this …

The success of machine learning across a wide array of tasks and applications has made it
appealing to use it also in the social domain. Indeed, learned models now form the …

Humans have come to rely on machines for reducing excessive information to manageable
representations. But this reliance can be abused–strategic machines might craft …

We propose a new variant of the strategic classification problem: a principal reveals a
classifier, and $ n $ agents report their (possibly manipulated) features to be classified …

Agents often have individual goals which depend on a group's actions. If agents trust a
forecast of collective action and adapt strategically, such prediction can influence outcomes …

When users stand to gain from certain predictions, they are prone to act strategically to
obtain favorable predictive outcomes. Whereas most works on strategic classification …