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 years, sparking important conversations about how to ensure fair outcomes from these systems.
Growing use of machine learning in policy and social impact settings have raised concerns for fairness implications, especially for racial minorities.
Explainability is a crucial requirement for effectiveness as well as the adoption of Machine Learning (ML) models supporting decisions in high-stakes public policy areas such as health, criminal justice, education, and employment, While the field of explainable has expanded in recent years, much of this work has not taken real-world needs into account.
no code implementations • 27 Aug 2020 • Isabelle Tingzon, Niccolo Dejito, Ren Avell Flores, Rodolfo De Guzman, Liliana Carvajal, Katerine Zapata Erazo, Ivan Enrique Contreras Cala, Jeffrey Villaveces, Daniela Rubio, Rayid Ghani
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.
Computers and Society
Rough sleeping is a chronic problem faced by some of the most disadvantaged people in modern society.
1 code implementation • 1 Jun 2020 • Avishek Kumar, Arthi Ramachandran, Adolfo De Unanue, Christina Sung, Joe Walsh, John Schneider, Jessica Ridgway, Stephanie Masiello Schuette, Jeff Lauritsen, Rayid Ghani
51% of PLWH are non-adherent with their medications and eventually drop out of medical care.
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.
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 practical problems.
no code implementations • 21 Dec 2018 • Sebastian Vollmer, Bilal A. Mateen, Gergo Bohner, Franz J. Király, Rayid Ghani, Pall Jonsson, Sarah Cumbers, Adrian Jonas, Katherine S. L. McAllister, Puja Myles, David Granger, Mark Birse, Richard Branson, Karel GM Moons, Gary S Collins, John P. A. Ioannidis, Chris Holmes, Harry Hemingway
Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing sources of data for patient benefit.
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 possible characteristics.
1 code implementation • 9 May 2018 • Avishek Kumar, Syed Ali Asad Rizvi, Benjamin Brooks, R. Ali Vanderveld, Kevin H. Wilson, Chad Kenney, Sam Edelstein, Adria Finch, Andrew Maxwell, Joe Zuckerbraun, Rayid Ghani
A barrier to proactive maintenance is the city's inability to predict the risk of failure on parts of its infrastructure.