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Found 12 papers, 2 papers with code
Improving Fairness in Adaptive Social Exergames via Shapley Bandits
Our results indicate that our Shapley Bandits effectively mediates the Greedy Bandit Problem and achieves better user retention and motivation across the participants.
TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments
With the increased legislation around data privacy, federated learning (FL) has emerged as a promising technique that allows the clients (end-user) to collaboratively train deep learning (DL) models without transferring and storing the data in a centralized, third-party server.
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
To this end, we first conduct interviews with data scientists to understand what constitutes disagreement between explanations generated by different methods for the same model prediction, and introduce a novel quantitative framework to formalize this understanding.
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner.
Active Screening for Recurrent Diseases: A Reinforcement Learning Approach
However, given the limited number of health workers, only a small subset of the population can be visited in any given time period.
Fair Influence Maximization: A Welfare Optimization Approach
Under this framework, the trade-off between fairness and efficiency can be controlled by a single inequality aversion design parameter.
Equilibrium Characterization for Data Acquisition Games
We demonstrate a reduction from this potentially complicated action space to a one-shot, two-action game in which each firm only decides whether or not to buy the data.
Fair Algorithms for Learning in Allocation Problems
We formalize this fairness notion for allocation problems and investigate its algorithmic consequences.
A Convex Framework for Fair Regression
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems.
Fairness in Criminal Justice Risk Assessments: The State of the Art
Methods: We draw on the existing literatures in criminology, computer science and statistics to provide an integrated examination of fairness and accuracy in criminal justice risk assessments.
Fairness in Reinforcement Learning
We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards.
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs
This models the problem of predicting the behavior of a rational agent whose goals are known, but whose resources are unknown.
