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Strategic Apple Tasting
Keegan Harris · Chara Podimata · Steven Wu

Wed Dec 13 03:00 PM -- 05:00 PM (PST) @ Great Hall & Hall B1+B2 #1804
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 dealing with incentives, in many domains of interest (e.g. lending and hiring) the decision-maker only observes feedback regarding their policy for rounds in which they assign a positive decision to the agent; this type of feedback is often referred to as apple tasting (or one-sided) feedback. We formalize this setting as an online learning problem with apple-tasting feedback where a principal makes decisions about a sequence of $T$ agents, each of which is represented by a context that may be strategically modified. Our goal is to achieve sublinear strategic regret, which compares the performance of the principal to that of the best fixed policy in hindsight, if the agents were truthful when revealing their contexts. Our main result is a learning algorithm which incurs $\tilde{\mathcal{O}}(\sqrt{T})$ strategic regret when the sequence of agents is chosen stochastically. We also give an algorithm capable of handling adversarially-chosen agents, albeit at the cost of $\tilde{\mathcal{O}}(T^{(d+1)/(d+2)})$ strategic regret (where $d$ is the dimension of the context). Our algorithms can be easily adapted to the setting where the principal receives bandit feedback---this setting generalizes both the linear contextual bandit problem (by considering agents with incentives) and the strategic classification problem (by allowing for partial feedback).

Author Information

Keegan Harris (Carnegie Mellon University)
Chara Podimata (Massachusetts Institute of Technology)
Steven Wu (Carnegie Mellon University)
Steven Wu

I am an Assistant Professor in the School of Computer Science at Carnegie Mellon University. My broad research interests are in algorithms and machine learning. These days I am excited about: - Foundations of responsible AI, with emphasis on privacy and fairness considerations. - Interactive learning, including contextual bandits and reinforcement learning, and its interactions with causal inference and econometrics. - Economic aspects of machine learning, with a focus on learning in the presence of strategic agents.

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