Web: http://arxiv.org/abs/2204.07661

May 12, 2022, 1:11 a.m. | Venelin Kovatchev, Soumyajit Gupta, Anubrata Das, Matthew Lease

cs.CL updates on arXiv.org arxiv.org

Recent work has emphasized the importance of balancing competing objectives
in model training (e.g., accuracy vs. fairness, or competing measures of
fairness). Such trade-offs reflect a broader class of multi-objective
optimization (MOO) problems in which optimization methods seek Pareto optimal
trade-offs between competing goals. In this work, we first introduce a
differentiable measure that enables direct optimization of group fairness
(specifically, balancing accuracy across groups) in model training. Next, we
demonstrate two model-agnostic MOO frameworks for learning Pareto optimal
parameterizations …

accuracy arxiv detection fairness hate speech learning speech

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