An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks
arXiv:2012.04344
Abstract
Decision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques. However, many proposed XAI methods produce unverified outputs. Evaluation and verification are usually achieved with a visual interpretation by humans on individual images or text. In this preregistration, we propose an empirical study and benchmark framework to apply attribution methods for neural networks developed for images and text data on time series. We present a methodology to automatically evaluate and rank attribution techniques on time series using perturbation methods to identify reliable approaches.
arXiv comment5 Pages, 2 Pages References, Pre-registration workshop NeurIPS (2020), Vancouver, Canada
References in corpus (13)
- Adam: A Method for Stochastic Optimization
- Towards A Rigorous Science of Interpretable Machine Learning
- Axiomatic Attribution for Deep Networks
- Striving for Simplicity: The All Convolutional Net
- Learning Important Features Through Propagating Activation Differences
- Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
- SmoothGrad: removing noise by adding noise
- Sanity Checks for Saliency Maps
- Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
- An Empirical Evaluation of Deep Learning on Highway Driving
- A Benchmark for Interpretability Methods in Deep Neural Networks
- explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning
- Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead