all AI news
Metric-Free Individual Fairness in Online Learning. (arXiv:2002.05474v5 [cs.LG] UPDATED)
Jan. 14, 2022, 2:10 a.m. | Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu
cs.LG updates on arXiv.org arxiv.org
We study an online learning problem subject to the constraint of individual
fairness, which requires that similar individuals are treated similarly. Unlike
prior work on individual fairness, we do not assume the similarity measure
among individuals is known, nor do we assume that such measure takes a certain
parametric form. Instead, we leverage the existence of an auditor who detects
fairness violations without enunciating the quantitative measure. In each
round, the auditor examines the learner's decisions and attempts to identify …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Senior AI Engineer, EdTech (Remote)
@ Lightci | Toronto, Ontario
Data Scientist for Salesforce Applications
@ ManTech | 781G - Customer Site,San Antonio,TX
AI Research Scientist
@ Gridmatic | Cupertino, CA
Data Engineer
@ Global Atlantic Financial Group | Boston, Massachusetts, United States
Machine Learning Engineer - Conversation AI
@ DoorDash | Sunnyvale, CA; San Francisco, CA; Seattle, WA; Los Angeles, CA