Rayid Ghani
Biographic Data
| ID | 5722049 |
|---|---|
| NAME | Rayid Ghani |
| GIVEN NAMES | Rayid |
| FAMILY NAME | Ghani |
| SIGNATURE | GHANI R |
| AFFILIATIONS | Ian Pan is with the Department of Biostatistics, School of Public Health, Brown University, Providence, RI. Laura B. Nolan is with the Population Research Center, School of Social Work, Columbia University, New York, NY. Rashida R. Brown is with the Division of Epidemiology, School of Public Health, University of California, Berkeley. Romana Khan is with the Kellogg School of Management, Northwestern University, Evanston, IL. Paul van der Boor and Rayid Ghani are with the Center for Data Science and... |
| ORCID | 0000-0003-0235-1843 |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 2 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 1 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
Beyond Implicit Bias
In their introduction to this edition of Dædalus, Goodwin Liu and Camara Phyllis Jones write that “it is unlikely that implicit bias can be effectively addressed by cognitive interventions alone, without broader institutional, legal, and structural reforms.” They note that the genesis for the volume was a March 2021 workshop on the science of implicit bias convened by the Committee on Science, Technology, and Law of the National Academies of Scie…
Bias and Fairness
This chapter discusses sources of potential bias in the modeling pipeline, as well as some of the ways that bias introduced by a model can be measured, with a particular focus on classification problems. Interest in algorithmic fairness and bias has been growing recently, but it is easy to become lost in the large number of definitions and metrics. Related to the sample bias discussed in bias sources, understanding the relevant population for mac…
Big Data and Social Science
Machine Learning for Social Services
Objectives. To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services. Methods. We used administrative data for 6457 women collected by the Illinois Department of Human Services from July 2014 to May 2015 to develop a machine learning model for adverse birth prediction and improve upon the existing paper-based r…
Machine Learning for Social Services
Objectives. To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services. Methods. We used administrative data for 6457 women collected by the Illinois Department of Human Services from July 2014 to May 2015 to develop a machine learning model for adverse birth prediction and improve upon the existing paper-based r…
Machine Learning for Social Services
Objectives. To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services. Methods. We used administrative data for 6457 women collected by the Illinois Department of Human Services from July 2014 to May 2015 to develop a machine learning model for adverse birth prediction and improve upon the existing paper-based r…
Bias and Fairness
This chapter discusses sources of potential bias in the modeling pipeline, as well as some of the ways that bias introduced by a model can be measured, with a particular focus on classification problems. Interest in algorithmic fairness and bias has been growing recently, but it is easy to become lost in the large number of definitions and metrics. Related to the sample bias discussed in bias sources, understanding the relevant population for mac…
Big Data and Social Science
Beyond Implicit Bias
In their introduction to this edition of Dædalus, Goodwin Liu and Camara Phyllis Jones write that “it is unlikely that implicit bias can be effectively addressed by cognitive interventions alone, without broader institutional, legal, and structural reforms.” They note that the genesis for the volume was a March 2021 workshop on the science of implicit bias convened by the Committee on Science, Technology, and Law of the National Academies of Scie…
Computer Science (4 works) · Artificial Intelligence (1 work) · Artificial Intelligence (1 work) · Big data (1 work) · Big data (1 work) · Big Data and Business Intelligence (1 work) · Computer security (1 work) · Data mining (1 work) · Data mining (1 work) · Data processing (1 work)