Introduction to Explainable AI

Author:

Liao Q. Vera1,Singh Moninder2,Zhang Yunfeng1,Bellamy Rachel3

Affiliation:

1. IBM Research, United States

2. IBM T.J. Watson Research Center, United States

3. IBM T.J. Watson Research, United States

Publisher

ACM

Reference8 articles.

1. Vijay Arya Rachel KE Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind Samuel C Hoffman Stephanie Houde Q Vera Liao Ronny Luss Aleksandra Mojsilović 2019. One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. arXiv preprint arXiv:1909.03012(2019). Vijay Arya Rachel KE Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind Samuel C Hoffman Stephanie Houde Q Vera Liao Ronny Luss Aleksandra Mojsilović 2019. One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. arXiv preprint arXiv:1909.03012(2019).

2. Machine Learning Interpretability;Carvalho V;A Survey on Methods and Metrics. Electronics,2019

3. Explaining models

4. Bringing Transparency Design into Practice

5. FICO. 2018. FICO Explainable Machine Learning Challenge. https://community.fico.com/s/explainable-machine-learning-challenge. Last accessed 2019-08. FICO. 2018. FICO Explainable Machine Learning Challenge. https://community.fico.com/s/explainable-machine-learning-challenge. Last accessed 2019-08.

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