Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …

We initiate the study of $\textit {behavioral information design} $ through deep learning. In
information design, a $\textit {sender} $ aims to persuade a $\textit {receiver} $ to take certain …

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 …

Personalized pricing, which involves tailoring prices based on individual characteristics, is
commonly used by firms to implement a consumer-specific pricing policy. In this process …

Machine learning models are often used to decide who will receive a loan, a job interview,
or a public benefit. Standard techniques to build these models use features about people but …

When humans are subject to an algorithmic decision system, they can strategically adjust
their behavior accordingly (``game''the system). While a growing line of literature on strategic …

We consider the problem of decision-making using panel data, in which a decision-maker
gets noisy, repeated measurements of multiple units (or agents). We consider the setup …

We investigate algorithmic decision problems where agents can respond strategically to the
decision maker's (DM) models. The demand for clear and actionable explanations from DMs …

In strategic classification, agents modify their features, at a cost, to ideally obtain a positive
classification from the learner's classifier. The typical response of the learner is to carefully …

This paper introduces Personalized Path Recourse, a novel method that generates recourse
paths for an agent. The objective is to achieve desired goals (eg, better outcomes compared …