Shapley Values are concepts established for eXplainable AI. They are used to explain black-box predictive models by quantifying the features' contributions to the model's outcomes. Since computing the exact Shapley Values is known to be computationally intractable on real-world datasets, neural estimators have emerged as alternative, more scalable approaches to get approximated Shapley Values estimates. However, experiments with neural estimators are currently hard to replicate as algorithm implementations, explainer evaluators, and results visualizations are neither standardized nor promptly usable. To bridge this gap, we present BONES, a new benchmark focused on neural estimation of Shapley Value. It provides researchers with a suite of state-of-the-art neural and traditional estimators, a set of commonly used benchmark datasets, ad hoc modules for training black-box models, as well as specific functions to easily compute the most popular evaluation metrics and visualize results. The purpose is to simplify XAI model usage, evaluation, and comparison. In this paper, we showcase BONES results and visualizations for XAI model benchmarking on both tabular and image data. The open-source library is available at the following link: https://github.com/DavideNapolitano/BONES.
Energy-Based Model for Accurate Estimation of Shapley Values in Feature Attribution
College of Metrology Measurement and Instrument, China Jiliang University（计量测量与仪器学院，中国浙江理工大学）；School of Mathematics, Hangzhou Normal University（数学学院，杭州师范大学）；School of Statistics, Jiangxi University of Finance and Economics（统计学院，江西财经大学）
Variance reduced Shapley value estimation for trustworthy data valuation
Virginia Tech（弗吉尼亚理工大学）；China Aerospace Academy of Systems Science and Engineering（中国航天系统科学与工程研究院）；Southern University of Science and Technology（南方科技大学）
Variational Inference and Bayesian CNNs for Uncertainty Estimation in Multi-Factorial Bone Age Prediction
Institute of Computer Graphics and Vision（计算机图形与视觉研究所）；Graz University of Technology（格拉茨理工大学）；School of Computer Science（计算机科学学院）；University of Auckland（奥克兰大学）
Amortized Linear-time Exact Shapley Value for Product-Kernel Methods
Rational Intelligence Lab, CISPA Helmholtz Center for Information Security（理性智能实验室，信息安全中心（CISPA））；Department of Computer Science, Vrije Universiteit Amsterdam（阿姆斯特丹自由大学计算机科学系）；Epistemic Intelligence & Computation Lab, College of Computing & Data Science, Nanyang Technological University（认知智能与计算实验室，南洋理工大学计算机与数据科学学院）
ShaplEIG: Bayesian Experimental Design for Shapley Value Estimation
Department of Statistics, LMU Munich（LMU慕尼黑统计系）；Institute for Informatics, LMU Munich（LMU慕尼黑信息学院）；Department of Computer Science, TU Dortmund University（图腾大学计算机科学系）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Lamarr Institute for Machine Learning（拉马尔机器学习研究所）
Humboldt University of Berlin（柏林洪堡大学）；Singapore Management University（新加坡管理大学）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Charles University（查理大学）；Institute for Digital Assets（数字资产研究院）
Department of Statistics, LMU Munich（LMU慕尼黑统计系）；Department of Electrical Engineering and Computer Science, UC Berkeley（伯克利电子工程与计算机科学系）；Mathematical Sciences Department, Claremont McKenna College（克莱门茨麦肯纳学院数学科学系）
Shapley Value Estimation for Multi-Site Data with Blockwise-Missing Features
Centre for Biomedical Data Science, Duke-NUS Medical School（杜克-新加坡国立大学医学院生物医学数据科学中心）；School of Data Science, Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Columbia University（哥伦比亚大学）；National University of Singapore（新加坡国立大学）；Peking University（北京大学）
Data Valuation by Fusing Global and Local Statistical Information
Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong Baptist University（香港 Baptist大学）；Renmin University of China（中国人民大学）
Owen Sampling Accelerates Contribution Estimation in Federated Learning
Department of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada（电气与软件工程系，卡里尔大学）；Department of Electrical Engineering and Computer Science, York University, Toronto, Ontario, Canada（电气工程与计算机科学系，约克大学）；School of Information, University of Michigan, Ann Arbor, Michigan, USA（信息学院，密歇根大学）
