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.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/garygsw/smooth-taylor
https://github.com/gewanying/shap-anti-spoofing
https://github.com/h2oai/xai_guidelines
Proposed Guidelines for the Responsible Use of Explainable Machine Learning
https://github.com/hannamw/eap-ig
https://github.com/ipolharvard/readmit-stgnn
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/jankrepl/mildlyoverfitted
https://github.com/jemilc/shap
https://github.com/jettify/ibreakdown
https://github.com/jphall663/hc_ml
Proposed Guidelines for the Responsible Use of Explainable Machine Learning
https://github.com/jphall663/interpretable_machine_learning_with_python
Proposed Guidelines for the Responsible Use of Explainable Machine Learning
https://github.com/jpmorganchase/cf-shap-facct22
https://github.com/koren-v/Interpret
https://github.com/luulinh90s/CFD
Explaining How Deep Neural Networks Forget by Deep Visualization
https://github.com/maheepchaudhary/sae-ravel
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations
https://github.com/manmeet3591/python_class
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset
https://github.com/marnifora/magisterka
https://github.com/miaolan-xie/shap
https://github.com/mindspore-ai/contrib/tree/master/intern/Integrated-Gradient
https://github.com/mpimd-csc/RK4-SinDy
https://github.com/nsaphra/acd
https://github.com/pamflecista/Magisterka
https://github.com/pietrobarbiero/logic_explained_networks
https://github.com/pwc-1/Paper-10/tree/main/t5
https://github.com/pwc-1/Paper-9/tree/main/4/Integrated-Gradient
https://github.com/pytorch/captum
https://github.com/realmoana/proxyexplainer
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/saivarunr/xshap
https://github.com/same2023neurips/same
https://github.com/sebastian-lapuschkin/explaining-deep-clinical-gait-classification
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis
https://github.com/sebastian-lapuschkin/interpretable-deep-gait-injury
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis
https://github.com/sebastian-lapuschkin/lrp_toolbox
Towards Best Practice in Explaining Neural Network Decisions with LRP
https://github.com/shaoshanglqy/shap-shapley
https://github.com/shap/shap
https://github.com/shenchenyi/SpeakerData
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
https://github.com/shuheng-li/units-sensory-time-series-classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.