2 citations · 5 across the 6 of their papers we have counts for
6 papers
End-to-End Fairness Optimization with Fair Decision-Focused Learning
Yu Wang, Violet Xinying Chen
Many real-world systems rely on predictive models to inform decisions, and fairness concerns arise in both the prediction and decision stages. We introduce end-to-end fairness opti…
Assessing Group Fairness with Social Welfare Optimization
Violet Chen, J. N. Hooker, Derek Leben
Statistical parity metrics have been widely studied and endorsed in the AI community as a means of achieving fairness, but they suffer from at least two weaknesses. They disregard…
Local Causal Discovery for Structural Evidence of Direct Discrimination
Jacqueline Maasch, Kyra Gan, Violet Chen +3
Identifying the causal pathways of unfairness is a critical objective for improving policy design and algorithmic decision-making. Prior work in causal fairness analysis often requ…
Fairness through Social Welfare Optimization
Violet Xinying Chen, J. N. Hooker
We propose social welfare optimization as a general paradigm for formalizing fairness in AI systems. We argue that optimization models allow formulation of a wide range of fairness…
Online Convex Optimization Perspective for Learning from Dynamically Revealed Preferences
Violet Xinying Chen, Fatma Kılınç-Karzan
We study the problem of online learning (OL) from revealed preferences: a learner wishes to learn a non-strategic agent's private utility function through observing the agent's uti…
Balancing Fairness and Efficiency in an Optimization Model
Violet Xinying Chen, J. N. Hooker
Optimization models generally aim for efficiency by maximizing total benefit or minimizing cost. Yet a trade-off between fairness and efficiency is an important element of many pra…