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AI Explainability 360: Impact and Design

AI Explainability 360: Impact and Design

24 September 2021
Vijay Arya
Rachel K. E. Bellamy
Pin-Yu Chen
Amit Dhurandhar
Michael Hind
Samuel C. Hoffman
Stephanie Houde
Q. V. Liao
Ronny Luss
Aleksandra Mojsilović
Sami Mourad
Pablo Pedemonte
Ramya Raghavendra
John T. Richards
P. Sattigeri
Karthikeyan Shanmugam
Moninder Singh
Kush R. Varshney
Dennis L. Wei
Yunfeng Zhang
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Papers citing "AI Explainability 360: Impact and Design"

3 / 3 papers shown
Title
An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled
  Explanations of AI Systems
An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled Explanations of AI Systems
Shruthi Chari
29
0
0
23 Oct 2024
Auditing and Generating Synthetic Data with Controllable Trust
  Trade-offs
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
Brian M. Belgodere
Pierre L. Dognin
Adam Ivankay
Igor Melnyk
Youssef Mroueh
...
Mattia Rigotti
Jerret Ross
Yair Schiff
Radhika Vedpathak
Richard A. Young
32
12
0
21 Apr 2023
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
257
3,690
0
28 Feb 2017
1