Publications (10)
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…
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…
Locating and measuring marine aquaculture production from space: a computer vision approach in the French Mediterranean
Sebastian Quaade, Andrea Vallebueno, Olivia D. N. Alcabes +2
Aquaculture production -- the cultivation of aquatic plants and animals -- has grown rapidly since the 1990s, but sparse, self-reported and aggregate production data limits the eff…
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…
Aequitas: A Bias and Fairness Audit Toolkit
Pedro Saleiro, Benedict Kuester, Loren Hinkson +5
Recent work has raised concerns on the risk of unintended bias in AI systems being used nowadays that can affect individuals unfairly based on race, gender or religion, among other…
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…
Artificial Intelligence in Environmental Protection: The Importance of Organizational Context from a Field Study in Wisconsin
Nicolas Rothbacher, Kit T. Rodolfa, Mihir Bhaskar +3
Advances in Artificial Intelligence (AI) have generated widespread enthusiasm for the potential of AI to support our understanding and protection of the environment. As such tools…
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…
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…
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…