AI Explainability 360 Toolkit for Time-Series and Industrial Use Cases

Author:

Ganapavarapu Giridhar1ORCID,Mukherjee Sumanta2ORCID,Martinez Gil Natalia1ORCID,Sarpatwar Kanthi1ORCID,Rajasekharan Amaresh3ORCID,Dhurandhar Amit1ORCID,Arya Vijay4ORCID,Vaculin Roman1ORCID

Affiliation:

1. IBM Research, Yorktown Heights, NY, USA

2. IBM Research, Bengaluru, India

3. IBM Software, Poughkeepsie, NY, USA

4. IBM Research, Gurugram, India

Publisher

ACM

Reference15 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, et al. 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, et al. 2019. One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques. arXiv preprint arXiv:1909.03012 (2019).

2. AI explainability 360

3. AI Explainability 360: Impact and Design

4. Tutorial on Human-Centered Explainability for Healthcare

5. Amit Dhurandhar , Pin-Yu Chen , Ronny Luss , Chun-Chen Tu , Paishun Ting , Karthikeyan Shanmugam , and Payel Das . 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. Advances in neural information processing systems , Vol. 31 ( 2018 ). Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das. 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. Advances in neural information processing systems, Vol. 31 (2018).

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