Incorporating Interpretable Output Constraints in Bayesian Neural Networks

• URL: http://arxiv.org/abs/2010.10969v2
• Date: Wed, 6 Jan 2021 10:07:56 GMT
• Title: Incorporating Interpretable Output Constraints in Bayesian Neural Networks
• Authors: Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez
• Abstract summary: Output-Constrained BNN (OC-BNN) is fully consistent with the Bayesian framework for uncertainty quantification. We demonstrate the efficacy of OC-BNNs on real-world datasets, spanning multiple domains such as healthcare, criminal justice, and credit scoring.
• Score: 34.103445420814644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness. We introduce a novel probabilistic framework for reasoning with such constraints and formulate a prior that enables us to effectively incorporate them into Bayesian neural networks (BNNs), including a variant that can be amortized over tasks. The resulting Output-Constrained BNN (OC-BNN) is fully consistent with the Bayesian framework for uncertainty quantification and is amenable to black-box inference. Unlike typical BNN inference in uninterpretable parameter space, OC-BNNs widen the range of functional knowledge that can be incorporated, especially for model users without expertise in machine learning. We demonstrate the efficacy of OC-BNNs on real-world datasets, spanning multiple domains such as healthcare, criminal justice, and credit scoring.

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