Paper

Incorporating Interpretable Output Constraints in Bayesian Neural Networks

Lorch, Lars, Doshi-Velez, Finale, Yang, Wanqian, Lakkaraju, Himabindu, Graule, Moritz A

2021

Overview

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.

• Publisher: Cornell University Library, arXiv.org
• Subject: Bayesian analysis / Constraints / Crime / Domains / Inference / Machine learning / Neural networks