Designing Inherently Interpretable Machine Learning Models
- URL: http://arxiv.org/abs/2111.01743v1
- Date: Tue, 2 Nov 2021 17:06:02 GMT
- Title: Designing Inherently Interpretable Machine Learning Models
- Authors: Agus Sudjianto and Aijun Zhang
- Abstract summary: Inherently IML models should be adopted because of their transparency and explainability.
Black-box models with model-agnostic explainability can be more difficult to defend under regulatory scrutiny.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Interpretable machine learning (IML) becomes increasingly important in highly
regulated industry sectors related to the health and safety or fundamental
rights of human beings. In general, the inherently IML models should be adopted
because of their transparency and explainability, while black-box models with
model-agnostic explainability can be more difficult to defend under regulatory
scrutiny. For assessing inherent interpretability of a machine learning model,
we propose a qualitative template based on feature effects and model
architecture constraints. It provides the design principles for
high-performance IML model development, with examples given by reviewing our
recent works on ExNN, GAMI-Net, SIMTree, and the Aletheia toolkit for local
linear interpretability of deep ReLU networks. We further demonstrate how to
design an interpretable ReLU DNN model with evaluation of conceptual soundness
for a real case study of predicting credit default in home lending. We hope
that this work will provide a practical guide of developing inherently IML
models in high risk applications in banking industry, as well as other sectors.
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