133 citations · 152 across the 3 of their papers we have counts for
6 papers
Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification
Robert Adragna, Elliot Creager, David Madras +1
Robustness is of central importance in machine learning and has given rise to the fields of domain generalization and invariant learning, which are concerned with improving perform…
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Martin Mladenov, Elliot Creager, Omer Ben-Porat +3
Most recommender systems (RS) research assumes that a user's utility can be maximized independently of the utility of the other agents (e.g., other users, content providers). In re…
Causal Modeling for Fairness in Dynamical Systems
Elliot Creager, David Madras, Toniann Pitassi +1
In many application areas---lending, education, and online recommenders, for example---fairness and equity concerns emerge when a machine learning system interacts with a dynamical…
Flexibly Fair Representation Learning by Disentanglement
Elliot Creager, David Madras, Jörn-Henrik Jacobsen +4
We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes. Taking inspiration from the disentangled…
Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data
David Madras, Elliot Creager, Toniann Pitassi +1
How do we learn from biased data? Historical datasets often reflect historical prejudices; sensitive or protected attributes may affect the observed treatments and outcomes. Classi…
Learning Adversarially Fair and Transferable Representations
David Madras, Elliot Creager, Toniann Pitassi +1
In this paper, we advocate for representation learning as the key to mitigating unfair prediction outcomes downstream. Motivated by a scenario where learned representations are use…