Recently, the term explainable AI became known as an approach to produce models from artificial intelligence which allow interpretation. Since a long time, there are models of symbolic regression in use that are perfectly explainable and mathematically tractable: in this contribution we demonstrate how to use symbolic regression methods to infer the optimal control of a dynamical system given one or several optimization criteria, or cost functions. In previous publications, network control was achieved by automatized machine learning control using genetic programming. Here, we focus on the subsequent analysis of the analytical expressions which result from the machine learning. In particular, we use AUTO to analyze the stability properties of the controlled oscillator system which served as our model. As a result, we show that there is a considerable advantage of explainable models over less accessible neural networks.
Empirical Characterization of Rationale Stability Under Controlled Perturbations for Explainable Pattern Recognition
The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Department of Computer Science, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校计算机科学系）；Department of Mechanical, Aerospace, and Industrial Engineering, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校机械、航空航天与工业工程系）
Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems
department of Electrical and Electronics Engineering, Koc University（电子与电气工程系，科克大学）；Computer, Electrical, and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology（计算机、电气和数学科学与工程系，国王阿卜杜勒-阿齐兹大学）
Exploring deterministic frequency deviations with explainable AI
Forschungszentrum Jülich（于利希研究中心）；University of Cologne（科隆大学）；Queen Mary University of London（伦敦玛丽女王大学）；Norwegian University of Life Sciences（挪威生命科学大学）
BenSParX: A Robust Explainable Machine Learning Framework for Parkinson's Disease Detection from Bengali Conversational Speech
Chittagong University of Engineering and Technology（吉大港工程技术大学）；East Delta University（东三角洲大学）；Charles Sturt University（查尔斯特大学）；Bangabandhu Sheikh Mujib Medical University（班加班杜·谢赫·穆吉布医科大学）
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks
Department of Electrical and Computer Engineering, University of Illinois at Chicago（伊利诺伊大学芝加哥分校电气与计算机工程系）；Kitware Inc.（Kitware公司）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Secondary control activation analysed and predicted with explainable AI
Forschungszentrum Jülich（于利希研究中心）；University of Cologne（科隆大学）；Queen Mary University of London（伦敦玛丽女王大学）；Norwegian University of Life Sciences（挪威生命科学大学）
PySS3: A Python package implementing a novel text classifier with visualization tools for Explainable AI
Universidad Nacional de San Luis（圣路易斯国立大学）；Consejo Nacional de Investigaciones Científicas y Técnicas（国家科学技术研究委员会）；Instituto Nacional de Astrofísica, Óptica y Electrónica（国家天体物理、光学和电子研究所）
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science
Technische Universität Berlin（柏林工业大学）；ATB（莱布尼茨农业工程与生物经济研究所）；University of Leipzig（莱比锡大学）；University of Reading（雷丁大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所（BIFOLD））；UiT the Arctic University of Norway（挪威北极大学）；University of Potsdam（波茨坦大学）
Uncertainty Gating for Cost-Aware Explainable Artificial Intelligence
Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB)（莱布尼茨农业工程与生物经济研究所）；BIFOLD -- Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Charité -- Universitätsmedizin Berlin（柏林夏里特医学院）；University of Potsdam -- Department of Computational Science（波茨坦大学计算科学系）
Uncertainty Awareness and Trust in Explainable AI- On Trust Calibration using Local and Global Explanations
Research Center Trustworthy Data Science and Security（可信数据科学与安全研究所以）；TU Dortmund University（图卢兹大学）；University Duisburg-Essen（杜伊斯堡- Essen大学）；University of Nottingham（诺丁汉大学）
ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment
Information Security Lab, University of Information Technology（信息安全部，信息科技大学）；Vietnam National University（越南国家大学）；School of Computer Science and Information Technology, Adelaide University（计算机科学与信息技术学院，阿德莱德大学）
Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version
Aalborg University（奥尔堡大学）；Huawei Cloud Database Innovation Lab（华为云数据库创新实验室）；University of Electronic Science and Technology of China（电子科技大学）
时间序列数据广泛存在，异常点检测是数据采掘的一个基本问题，有着广泛的应用。现有的基于自动编码器的方法在具有挑战性的现实世界数据上提供了最先进的性能，但容易受到离群值的影响，并且表现出较低的可解释性。为了解决这两个限制，我们提出了鲁棒的和可解释的无监督自动编码器框架，使用自动编码器将输入时间序列分解为干净的时间序列和离群值时间序列。由于干净的时间序列可以用易于理解的模式（如趋势和周期性）更好地解释，因此可解释性得到了改善。我们通过事后可解释性分析和实证研究对此进行了深入研究。此外，由于离群值是迭代地从干净的时间序列中分离出来的，我们的方法对离群值提供了更好的鲁棒性，这反过来又提高了准确性。我们在五个真实世界的数据集上评估了我们的方法，并报告了在鲁棒性和可解释性方面对最先进方法的改进。这是“Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection”的扩展版本，将出现在IEEE ICDE 2022中。
