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/shyhyawJou/Integrated-Gradient-Tensorflow
https://github.com/sicara/tf-explain
https://github.com/sooeun67/xai
https://github.com/soyoung9306/3D_CNN
https://github.com/suinleelab/path_explain
https://github.com/tleemann/road_evaluation
https://github.com/tomdyer10/fake_news
https://github.com/tsy935/readmit-stgnn
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/uhussai7/boldreams
https://github.com/visinf/funnybirds-framework
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods
https://github.com/xiaoyanLi629/ScRNA-seq-integration-by-Heterogeneous-Graph-transformer-neural-network
https://github.com/yeefan1999/Explainable-Health-Prediction-with-Transfer-Learning
https://github.com/ziqi-li/geoshapley
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.