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How to address monotonicity for model risk management?

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  • Dangxing Chen
  • Weicheng Ye

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.

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  • Dangxing Chen & Weicheng Ye, 2023. "How to address monotonicity for model risk management?," Papers 2305.00799, arXiv.org, revised Sep 2023.
    • Handle: RePEc:arx:papers:2305.00799
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    1. Dangxing Chen & Weicheng Ye, 2022. "Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring," Papers 2209.10070, arXiv.org.
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