69 citations · 142 across the 8 of their papers we have counts for
8 papers
Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools
Emily Black, Rakshit Naidu, Rayid Ghani +3
While algorithmic fairness is a thriving area of research, in practice, mitigating issues of bias often gets reduced to enforcing an arbitrarily chosen fairness metric, either by e…
A Conceptual Framework for Using Machine Learning to Support Child Welfare Decisions
Ka Ho Brian Chor, Kit T. Rodolfa, Rayid Ghani
Human services systems make key decisions that impact individuals in the society. The U.S. child welfare system makes such decisions, from screening-in hotline reports of suspected…
An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings
Hemank Lamba, Kit T. Rodolfa, Rayid Ghani
Applications of machine learning (ML) to high-stakes policy settings -- such as education, criminal justice, healthcare, and social service delivery -- have grown rapidly in recent…
Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy
Kit T. Rodolfa, Hemank Lamba, Rayid Ghani
Growing use of machine learning in policy and social impact settings have raised concerns for fairness implications, especially for racial minorities. These concerns have generated…
Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions
Kasun Amarasinghe, Kit Rodolfa, Hemank Lamba +1
Explainability is highly-desired in Machine Learning (ML) systems supporting high-stakes policy decisions in areas such as health, criminal justice, education, and employment. Whil…
Case Study: Predictive Fairness to Reduce Misdemeanor Recidivism Through Social Service Interventions
Kit T. Rodolfa, Erika Salomon, Lauren Haynes +3
The criminal justice system is currently ill-equipped to improve outcomes of individuals who cycle in and out of the system with a series of misdemeanor offenses. Often due to cons…