9 accepted papers
Crowdsourcing has been increasingly utilized to gather subjective assessment, such as evaluating the toxicity of texts. Since there doesnot exist a single “ground truth” answer for subjective annotations, obtaining annotations to accurately reflect the opinions of differentsubgroups becomes a key ob…
Performative prediction, as introduced by Perdomo et al., is a framework for studying social prediction in which the data distribution itself changes in response to the deployment of a model. Existing work in this field usually hinges on three assumptions that are easily violated in practice: that t…
We investigate perceptions of fairness in the choice of features that algorithms use about individuals in a simulated gigwork employment experiment. First, a collection of experimental participants (the selectors) were asked to recommend an algorithm for making employment decisions. Second, a differ…
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 actions by strategically revealing information. We address scenarios in which the receiver might exhibit differen…
We study the environment design problem for biased decision makers. In an environment design problem, an informed principal aims to update the decision making environment to influence the decisions made by the agent. This problem is ubiquitous in various domains, e.g., a social networking platform m…
We consider a stochastic multi-armed bandit (MAB) problem with delayed impact of actions. In our setting, actions taken in the past impact the arm rewards in the subsequent future. This delayed impact of actions is prevalent in the real world. For example, the capability to pay back a loan for peopl…
There is an increasing use of algorithms to inform decisions in many settings, from student evaluations, college admissions, to credit scoring. These decisions are made by applying a decision rule to individual’s observed features. Given the impacts of these decisions on individuals, decision makers…
We study the \emph{secure} stochastic convex optimization problem: a learner aims to learn the optimal point of a convex function through sequentially querying a (stochastic) gradient oracle, in the meantime, there exists an adversary who aims to free-ride and infer the learning outcome of the learn…
Models for collecting and aggregating categorical data on crowdsourcing platforms typically fall into two broad categories: those assuming agents honest and consistent but with heterogeneous error rates, and those assuming agents strategic and seek to maximize their expected reward. The former often…