💻 Related Software:¶

shapiq is a Library for computing Shapley Interactions and Shapley Values for Machine Learning [Muschalik et al., 2024]. The following table contains a list of related software:

Software	Citation	Description
`shapiq`	[Muschalik et al., 2024]	This Python library for interpreting machine learning models with general game-theoretic concpts such as Shapley interactions, Shapley values, or Banzhaf values.
`shap`	[Lundberg and Lee, 2017]	A Python library for interpreting machine learning models, including Shapley values.
`OpenXAI`	[Agarwal et al., 2022]	A benchmark suite for Explainable AI
`iNNvestigate`	[Alber et al., 2019]	An Interpretability Toolkit for Neural Networks
`aix360`	[Arya et al., 2020]	An Extensible Toolkit for Understanding Data and Machine Learning Models
`dalex`	[Baniecki et al., 2021]	Responsible Machine Learning with Interactive Explainability and Fairness in Python
`openml`	[Bischl et al., 2021]	A general purpose Benchmarking Suites
`quantus`	[Hedström et al., 2023]	An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
`OpenDataVal`	[Jiang et al., 2023]	A a Unified Benchmark for Data Valuation
`alibi`	[Klaise et al., 2021]	General Algorithms for Explaining Machine Learning Models
`captum`	[Kokhlikyan et al., 2020]	A unified and generic model interpretability library for PyTorch
`XAI-bench`	[Liu et al., 2021]	Synthetic Benchmarks for Scientific Research in Explainable Machine Learning
`M4`	[Li et al., 2023]	A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models
	[Olsen et al., 2024]	A comparative study of methods for estimating model-agnostic Shapley value explanations