Multicriteria interpretability driven Deep Learning

• URL: http://arxiv.org/abs/2111.14088v1
• Date: Sun, 28 Nov 2021 09:41:13 GMT
• Title: Multicriteria interpretability driven Deep Learning
• Authors: Marco Repetto
• Abstract summary: Deep Learning methods are renowned for their performances, yet their lack of interpretability prevents them from high-stakes contexts. Recent model methods address this problem by providing post-hoc interpretability methods by reverse-engineering the model's inner workings. We propose a Multicriteria agnostic technique that allows to control the feature effects on the model's outcome by injecting knowledge in the objective function.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Deep Learning methods are renowned for their performances, yet their lack of interpretability prevents them from high-stakes contexts. Recent model agnostic methods address this problem by providing post-hoc interpretability methods by reverse-engineering the model's inner workings. However, in many regulated fields, interpretability should be kept in mind from the start, which means that post-hoc methods are valid only as a sanity check after model training. Interpretability from the start, in an abstract setting, means posing a set of soft constraints on the model's behavior by injecting knowledge and annihilating possible biases. We propose a Multicriteria technique that allows to control the feature effects on the model's outcome by injecting knowledge in the objective function. We then extend the technique by including a non-linear knowledge function to account for more complex effects and local lack of knowledge. The result is a Deep Learning model that embodies interpretability from the start and aligns with the recent regulations. A practical empirical example based on credit risk, suggests that our approach creates performant yet robust models capable of overcoming biases derived from data scarcity.

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