Cargando…
Tuning Fairness by Balancing Target Labels
The issue of fairness in machine learning models has recently attracted a lot of attention as ensuring it will ensure continued confidence of the general public in the deployment of machine learning systems. We focus on mitigating the harm incurred by a biased machine learning system that offers bet...
Autores principales: | Kehrenberg, Thomas, Chen, Zexun, Quadrianto, Novi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861271/ https://www.ncbi.nlm.nih.gov/pubmed/33733151 http://dx.doi.org/10.3389/frai.2020.00033 |
Ejemplares similares
-
Causal Datasheet for Datasets: An Evaluation Guide for Real-World Data Analysis and Data Collection Design Using Bayesian Networks
por: Butcher, Bradley, et al.
Publicado: (2021) -
On Consequentialism and Fairness
por: Card, Dallas, et al.
Publicado: (2020) -
Sysrev: A FAIR Platform for Data Curation and Systematic Evidence Review
por: Bozada, Thomas, et al.
Publicado: (2021) -
Facing the Challenges of Developing Fair Risk Scoring Models
por: Szepannek, Gero, et al.
Publicado: (2021) -
A United States Fair Lending Perspective on Machine Learning
por: Hall, Patrick, et al.
Publicado: (2021)