69 citations · 305 across the 17 of their papers we have counts for
17 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…
Faking feature importance: A cautionary tale on the use of differentially-private synthetic data
Oscar Giles, Kasra Hosseini, Grigorios Mingas +13
Synthetic datasets are often presented as a silver-bullet solution to the problem of privacy-preserving data publishing. However, for many applications, synthetic data has been sho…
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…
Mapping New Informal Settlements using Machine Learning and Time Series Satellite Images: An Application in the Venezuelan Migration Crisis
Isabelle Tingzon, Niccolo Dejito, Ren Avell Flores +7
Since 2014, nearly 2 million Venezuelans have fled to Colombia to escape an economically devastated country during what is one of the largest humanitarian crises in modern history.…
Bandit Data-Driven Optimization
Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani +1
Applications of machine learning in the non-profit and public sectors often feature an iterative workflow of data acquisition, prediction, and optimization of interventions. There…
A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services
Harrison Wilde, Lucia Lushi Chen, Austin Nguyen +7
Rough sleeping is a chronic problem faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link, a UK-…
A Machine Learning System for Retaining Patients in HIV Care
Avishek Kumar, Arthi Ramachandran, Adolfo De Unanue +7
Retaining persons living with HIV (PLWH) in medical care is paramount to preventing new transmissions of the virus and allowing PLWH to live normal and healthy lifespans. Maintaini…
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…
A Clinical Approach to Training Effective Data Scientists
Kit T Rodolfa, Adolfo De Unanue, Matt Gee +1
Like medicine, psychology, or education, data science is fundamentally an applied discipline, with most students who receive advanced degrees in the field going on to work on pract…
Artificial Intelligence for Social Good
Gregory D. Hager, Ann Drobnis, Fei Fang +8
The Computing Community Consortium (CCC), along with the White House Office of Science and Technology Policy (OSTP), and the Association for the Advancement of Artificial Intellige…
Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness
Sebastian Vollmer, Bilal A. Mateen, Gergo Bohner +15
Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing s…
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…
Using Machine Learning to Assess the Risk of and Prevent Water Main Breaks
Avishek Kumar, Syed Ali Asad Rizvi, Benjamin Brooks +8
Water infrastructure in the United States is beginning to show its age, particularly through water main breaks. Main breaks cause major disruptions in everyday life for residents a…
Applying Machine Learning Methods to Enhance the Distribution of Social Services in Mexico
Kris Sankaran, Diego Garcia-Olano, Mobin Javed +7
The Government of Mexico's social development agency, SEDESOL, is responsible for the administration of social services and has the mission of lifting Mexican families out of pover…