33 citations · 117 across the 22 of their papers we have counts for
3 papers · 1 filter
Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results
Nathanael Jo, Bill Tang, Kathryn Dullerud +3
We study critical systems that allocate scarce resources to satisfy basic needs, such as homeless services that provide housing. These systems often support communities disproporti…
Learning Resource Allocation Policies from Observational Data with an Application to Homeless Services Delivery
Aida Rahmattalabi, Phebe Vayanos, Kathryn Dullerud +1
We study the problem of learning, from observational data, fair and interpretable policies that effectively match heterogeneous individuals to scarce resources of different types.…
Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy
Nathanael Jo, Sina Aghaei, Andrés Gómez +1
The increasing use of machine learning in high-stakes domains -- where people's livelihoods are impacted -- creates an urgent need for interpretable, fair, and highly accurate algo…