How to address monotonicity for model risk management?

Dangxing Chen and Weicheng Ye

Papers from arXiv.org

Abstract: In this paper, we study the problem of establishing the accountability and fairness of transparent machine learning models through monotonicity. Although there have been numerous studies on individual monotonicity, pairwise monotonicity is often overlooked in the existing literature. This paper studies transparent neural networks in the presence of three types of monotonicity: individual monotonicity, weak pairwise monotonicity, and strong pairwise monotonicity. As a means of achieving monotonicity while maintaining transparency, we propose the monotonic groves of neural additive models. As a result of empirical examples, we demonstrate that monotonicity is often violated in practice and that monotonic groves of neural additive models are transparent, accountable, and fair.

Date: 2023-04, Revised 2023-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
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Published in In Proceedings of the 40th International Conference on Machine Learning, 2023, (Proceedings of Machine Learning Research, Vol. 202). PMLR, 5282-5295

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