XAITK : The explainable AI toolkit

Author:

Hu Brian1ORCID,Tunison Paul1,Vasu Bhavan1,Menon Nitesh1,Collins Roddy1,Hoogs Anthony1

Affiliation:

1. Kitware, Inc. New York New York USA

Funder

Air Force Research Laboratory

Publisher

Wiley

Reference26 articles.

1. HolzingerA BiemannC PattichisCS KellDB. What do we need to build explainable AI systems for the medical domain? arXiv:1712.09923; 2017.

2. Doshi‐VelezF KimB. Towards a rigorous science of interpretable machine learning. arXiv:1702.08608; 2017.

3. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead;Rudin C;Nature Mach Intell,2019

4. SamekW WiegandT MüllerKR. Explainable artificial intelligence: understanding visualizing and interpreting deep learning models. ITU Journal: ICT Discoveries 2017(1).

5. Peeking inside the black‐box: a survey on explainable artificial intelligence (XAI);Adadi A;IEEE Access,2018

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