475 citations · 555 across the 14 of their papers we have counts for
14 papers
On the Cause of Unfairness: A Training Sample Perspective
Yuanshun Yao, Yang Liu
Identifying the causes of a model's unfairness is an important yet relatively unexplored task. We look into this problem through the lens of training data - the major source of unf…
Certifying Some Distributional Fairness with Subpopulation Decomposition
Mintong Kang, Linyi Li, Maurice Weber +3
Extensive efforts have been made to understand and improve the fairness of machine learning models based on observational metrics, especially in high-stakes domains such as medical…
Unintended Selection: Persistent Qualification Rate Disparities and Interventions
Reilly Raab, Yang Liu
Realistically -- and equitably -- modeling the dynamics of group-level disparities in machine learning remains an open problem. In particular, we desire models that do not suppose…
Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents
Andrew Estornell, Sanmay Das, Yang Liu +1
The use of algorithmic decision making systems in domains which impact the financial, social, and political well-being of people has created a demand for these decision making syst…
When Optimizing -divergence is Robust with Label Noise
Jiaheng Wei, Yang Liu
We show when maximizing a properly defined -divergence measure with respect to a classifier's predictions and the supervised labels is robust with label noise. Leveraging its va…
How Do Fair Decisions Fare in Long-term Qualification?
Xueru Zhang, Ruibo Tu, Yang Liu +4
Although many fairness criteria have been proposed for decision making, their long-term impact on the well-being of a population remains unclear. In this work, we study the dynamic…
Incentives for Federated Learning: a Hypothesis Elicitation Approach
Yang Liu, Jiaheng Wei
Federated learning provides a promising paradigm for collecting machine learning models from distributed data sources without compromising users' data privacy. The success of a cre…
Distributional Individual Fairness in Clustering
Nihesh Anderson, Suman K. Bera, Syamantak Das +1
In this paper, we initiate the study of fair clustering that ensures distributional similarity among similar individuals. In response to improving fairness in machine learning, rec…
Replication Markets: Results, Lessons, Challenges and Opportunities in AI Replication
Yang Liu, Michael Gordon, Juntao Wang +5
The last decade saw the emergence of systematic large-scale replication projects in the social and behavioral sciences, (Camerer et al., 2016, 2018; Ebersole et al., 2016; Klein et…
Sample Elicitation
Jiaheng Wei, Zuyue Fu, Yang Liu +3
It is important to collect credible training samples for building data-intensive learning systems (e.g., a deep learning system). Asking people to report complex distributi…
Reinforcement Learning with Perturbed Rewards
Jingkang Wang, Yang Liu, Bo Li
Recent studies have shown that reinforcement learning (RL) models are vulnerable in various noisy scenarios. For instance, the observed reward channel is often subject to noise in…
Actionable Recourse in Linear Classification
Berk Ustun, Alexander Spangher, Yang Liu
Machine learning models are increasingly used to automate decisions that affect humans - deciding who should receive a loan, a job interview, or a social service. In such applicati…
Randomized Wagering Mechanisms
Yiling Chen, Yang Liu, Juntao Wang
Wagering mechanisms are one-shot betting mechanisms that elicit agents' predictions of an event. For deterministic wagering mechanisms, an existing impossibility result has shown i…
Bayesian fairness
Christos Dimitrakakis, Yang Liu, David Parkes +1
We consider the problem of how decision making can be fair when the underlying probabilistic model of the world is not known with certainty. We argue that recent notions of fairnes…