March 12, 2024, 4:41 a.m. | Dalia Gala, Milo Phillips-Brown, Naman Goel, Carinal Prunkl, Laura Alvarez Jubete, medb corcoran, Ray Eitel-Porter

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.06031v1 Announce Type: new
Abstract: Machine learning requires defining one's target variable for predictions or decisions, a process that can have profound implications on fairness: biases are often encoded in target variable definition itself, before any data collection or training. We present an interactive simulator, FairTargetSim (FTS), that illustrates how target variable definition impacts fairness. FTS is a valuable tool for algorithm developers, researchers, and non-technical stakeholders. FTS uses a case study of algorithmic hiring, using real-world data and user-defined …

arxiv cs.ai cs.cy cs.lg definition effects fairness interactive type understanding

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Sr. Software Development Manager, AWS Neuron Machine Learning Distributed Training

@ Amazon.com | Cupertino, California, USA