5 citations · 9 across the 6 of their papers we have counts for
6 papers
Learning Who to Treat When Treatment is Missing
Johnna Sundberg, Rayid Ghani, Eli Ben-Michael +1
Policy learning methods are increasingly used to inform treatment allocation under budget constraints. Most proposed methods assume complete treatment data, yet applications freque…
Toward Third-Party Assurance of AI Systems: Design Requirements, Prototype, and Early Testing
Rachel M. Kim, Blaine Kuehnert, Alice Lai +3
As Artificial Intelligence (AI) systems proliferate, the need for systematic, transparent, and actionable processes for evaluating them is growing. While many resources exist to su…
Breaking the Cycle of Incarceration With Targeted Mental Health Outreach: A Case Study in Machine Learning for Public Policy
Kit T. Rodolfa, Erika Salomon, Jin Yao +9
Many incarcerated individuals face significant and complex challenges, including mental illness, substance dependence, and homelessness, yet jails and prisons are often poorly equi…
Aequitas Flow: Streamlining Fair ML Experimentation
Sérgio Jesus, Pedro Saleiro, Inês Oliveira e Silva +5
Aequitas Flow is an open-source framework and toolkit for end-to-end Fair Machine Learning (ML) experimentation, and benchmarking in Python. This package fills integration gaps tha…
Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance
Catalina Vajiac, Arun Frey, Joachim Baumann +5
Rental assistance programs provide individuals with financial assistance to prevent housing instabilities caused by evictions and avert homelessness. Since these programs operate u…
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods
Kasun Amarasinghe, Kit T. Rodolfa, Sérgio Jesus +6
Most existing evaluations of explainable machine learning (ML) methods rely on simplifying assumptions or proxies that do not reflect real-world use cases; the handful of more robu…