Explainable artificial intelligence
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XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://gitlab.science.ru.nl/gbucur/caushapley
공식 코드https://zenodo.org/record/13844552
공식 코드https://zenodo.org/record/5497955
공식 코드Exploring deterministic frequency deviations with explainable AI
https://github.com/Aditya239233/GNNExplainer
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/AlejandroAttento/Pytorch-Captum
https://github.com/AlexDuvalinho/GNNE_eval
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/Cherrydomini/Effects-of-feature-dropping-on-COMPAS-with-the-influence-of-LIME-
XAI-P-T: A Brief Review of Explainable Artificial Intelligence from Practice to Theory
https://github.com/FLE-ISP/CEILS
Counterfactual Explanations as Interventions in Latent Space
https://github.com/Imageomics/Prompt_CAM
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis
https://github.com/JepsonWong/CNN_Visualization
https://github.com/Jh-SYSU/MolRep
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction
https://github.com/JoHof/IntegratedGradientsTutorial
https://github.com/LarsHoldijk/RE-ParameterizedExplainerForGraphNeuralNetworks
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/MindSpore-scientific-2/code-4/tree/main/Integrated-Gradient
https://github.com/MindSpore-scientific/code-8/tree/main/Integrated-Gradient
https://github.com/MorenoLaQuatra/ARCH
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
https://github.com/OpenXAIProject/GNNExplainer-Tutorial
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/ShaharGottlieb/GNNExplainer_DGL
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/TianhongDai/integrated-gradient-pytorch
https://github.com/TooTouch/WhiteBox-Part1
https://github.com/VisiumCH/AMLD-2021-Graphs
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/YoruCathy/USDSeg-FCOS
Learning Universal Shape Dictionary for Realtime Instance Segmentation
https://github.com/allenwind/text-integrated-gradients
https://github.com/andresbecker/master_thesis
https://github.com/anshul3899/GNNExplainer-Experiments
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/ascillitoe/shap
https://github.com/atreyasha/spp-explainability
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines
https://github.com/austinbrown34/shap
https://github.com/bips-hb/innsight
https://github.com/cdpierse/transformers-interpret
https://github.com/d-becking/ECQx
https://github.com/deezer/functional_attribution
Towards Rigorous Interpretations: a Formalisation of Feature Attribution
https://github.com/dmlc/dgl/tree/master/examples/pytorch/gnn_explainer
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/fcUalberta/UAlberta-Multimedia-Masters-Program-Interpretable-AI-Part_1_2
https://github.com/gablabc/shap
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.