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AI explainability 360 : hands-on tutorial:

, In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency,

Arya, Vijay ; Bellamy, Rachel K. E. ; Chen, Pin-Yu... - p. 696 ff. , 2020

Link: https://dl.acm.org/doi/10.1145/3351095.3375667

RT 
T1 <cTitle>Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency</cTitle>: T1 <Title>AI explainability 360				: hands-on tutorial</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3375667&Exemplar=1&LAN=DE
				
A1 Arya, Vijay
A1 Bellamy, Rachel K. E.
A1 Chen, Pin-Yu
A1 Dhurandhar, Amit
A1 Hind, Michael
A1 Hoffman, Samuel C.
A1 Houde, Stephanie
A1 Liao, Q. Vera
A1 Luss, Ronny
A1 Mojsilović, Aleksandra
A1 Mourad, Sami
A1 Pedemonte, Pablo
A1 Raghavendra, Ramya
A1 Richards, John
A1 Sattigeri, Prasanna
A1 Shanmugam, Karthikeyan
A1 Singh, Moninder
A1 Varshney, Kush R.
A1 Wei, Dennis
A1 Zhang, Yunfeng
PB ACM
YR 2020
K1 explainability
K1 interpretability
K1 open source
K1 taxonomy
K1 transparency
K1 Computing methodologies
K1 Machine learning
K1 Machine learning approaches
K1 Classification and regression trees
K1 Kernel methods
K1 Learning latent representations
K1 Rule learning
K1 Instance-based learning
K1 Neural networks
SP 696 ff.
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3351095.3375667
DO https://dl.acm.org/doi/10.1145/3351095.3375667
SF ELIB - SuUB Bremen

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