# Explainable Machine Learning: The Importance of a System-Centric Perspective

arXiv: https://arxiv.org/abs/2302.02347 · HTML: https://mlsanity.com/paper/2302.02347/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 5 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Deep Unfolding Network for Image Super-Resolution](https://mlsanity.com/paper/2003.10428/) (2020)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [Explainable Deep Learning: A Field Guide for the Uninitiated](https://mlsanity.com/paper/2004.14545/) (2020)
- [Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics](https://mlsanity.com/paper/1612.07767/) (2016)
- [explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning](https://mlsanity.com/paper/1908.00087/) (2019)
- [Towards Explainable Deep Neural Networks (xDNN)](https://mlsanity.com/paper/1912.02523/) (2019)
- [PointHop: An Explainable Machine Learning Method for Point Cloud Classification](https://mlsanity.com/paper/1907.12766/) (2019)
- [Visual Analytics for Explainable Deep Learning](https://mlsanity.com/paper/1804.02527/) (2018)
- [Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology](https://mlsanity.com/paper/2008.01247/) (2020)
- [Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosis](https://mlsanity.com/paper/1902.06019/) (2019)
- [Two Applications of Deep Learning in the Physical Layer of Communication Systems](https://mlsanity.com/paper/2001.03350/) (2020)

## Abstract (arXiv)

The landscape in the context of several signal processing applications and even education appears to be significantly affected by the emergence of machine learning (ML) and in particular deep learning (DL).The main reason for this is the ability of DL to model complex and unknown relationships between signals and the tasks of interest. Particularly, supervised DL algorithms have been fairly successful at recognizing perceptually or semantically useful signal information in different applications. In all of these, the training process uses labeled data to learn a mapping function (typically implicitly) from signals to the desired information (class label or target label). The trained DL model is then expected to correctly recognize/classify relevant information in a given test signal. A DL based framework is therefore, in general, very appealing since the features and characteristics of the required mapping are learned almost exclusively from the data without resorting to explicit model/system development. The focus on implicit modeling however also raises the issue of lack of explainability/interpretability of the resultant DL based mapping or the black box problem. As a result, explainable ML/DL is an active research area where the primary goal is to elaborate how the ML/DL model arrived at a prediction. We however note that despite the efforts, the commentary on black box problem appears to lack a technical discussion from the view point of: a) its origin and underlying reasons, and b) its practical implications on the design and deployment of ML/DL systems. Accordingly, a reasonable question that can be raised is as follows. Can the traditional system-centric approach (which places emphasis on explicit system modeling) provide useful insights into the nature of black box problem, and help develop more transparent ML/DL systems?