23 citations · 44 across the 8 of their papers we have counts for
9 papers
AI-Assisted Decision Making with Human Learning
Gali Noti, Kate Donahue, Jon Kleinberg +1
AI systems increasingly support human decision-making. In many cases, despite the algorithm's superior performance, the final decision remains in human hands. For example, an AI ma…
When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-making
Kate Donahue, Sreenivas Gollapudi, Kostas Kollias
Historically, much of machine learning research has focused on the performance of the algorithm alone, but recently more attention has been focused on optimizing joint human-algori…
Private Blotto: Viewpoint Competition with Polarized Agents
Kate Donahue, Jon Kleinberg
Social media platforms are responsible for collecting and disseminating vast quantities of content. Recently, however, they have also begun enlisting users in helping annotate this…
Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness
Kate Donahue, Alexandra Chouldechova, Krishnaram Kenthapadi
Much of machine learning research focuses on predictive accuracy: given a task, create a machine learning model (or algorithm) that maximizes accuracy. In many settings, however, t…
Models of fairness in federated learning
Kate Donahue, Jon Kleinberg
In many real-world situations, data is distributed across multiple self-interested agents. These agents can collaborate to build a machine learning model based on data from multipl…
Optimality and Stability in Federated Learning: A Game-theoretic Approach
Kate Donahue, Jon Kleinberg
Federated learning is a distributed learning paradigm where multiple agents, each only with access to local data, jointly learn a global model. There has recently been an explosion…
Better Together? How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness
Kate Donahue, Solon Barocas
Consider a cost-sharing game with players of different contribution to the total cost: an example might be an insurance company calculating premiums for a population of mixed-risk…
Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation
Kate Donahue, Jon Kleinberg
Federated learning is a setting where agents, each with access to their own data source, combine models from local data to create a global model. If agents are drawing their data f…
Fairness and Utilization in Allocating Resources with Uncertain Demand
Kate Donahue, Jon Kleinberg
Resource allocation problems are a fundamental domain in which to evaluate the fairness properties of algorithms. The trade-offs between fairness and utilization have a long histor…