April 6, 2022, 1:11 a.m. | Yaowei Hu, Lu Zhang

cs.LG updates on arXiv.org arxiv.org

In this paper, we propose a framework for achieving long-term fair sequential
decision making. By conducting both the hard and soft interventions, we propose
to take path-specific effects on the time-lagged causal graph as a quantitative
tool for measuring long-term fairness. The problem of fair sequential decision
making is then formulated as a constrained optimization problem with the
utility as the objective and the long-term and short-term fairness as
constraints. We show that such an optimization problem can be converted …

arxiv decision decision making fairness long-term making

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Data Management Assistant

@ World Vision | Amman Office, Jordan

Cloud Data Engineer, Global Services Delivery, Google Cloud

@ Google | Buenos Aires, Argentina