Machine Learning

TIP: Typifying the Interpretability of Procedures

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  • arXiv
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    TIP: Typifying the Interpretability of Procedures

    We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an absolute concept and so we define it relative to a target model, which may or may not be a human. We define a framework that allows for comparing interpretable procedures by linking it to important practical aspects such as accuracy and robustness. We characterize many of the current state-of-the-art interpretable methods in our framework portraying its general applicability. Finally, principled interpretable strategies are proposed and empirically evaluated on synthetic data, as well as on the largest public olfaction dataset that was made recently available cite{olfs}. We also experiment on MNIST with a simple target model and different oracle models of varying complexity. This leads to the insight that the improvement in the target model is not only a function of the oracle models performance, but also its relative complexity with respect to the target model.

    TIP: Typifying the Interpretability of Procedures
    by Amit Dhurandhar, Vijay Iyengar, Ronny Luss, Karthikeyan Shanmugam

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