Building Trust in Interactive Machine Learning via User Contributed Interpretable Rules-Reference-Cited by-同舟云学术

Building Trust in Interactive Machine Learning via User Contributed Interpretable Rules

Published:2022-03-22 Issue: Volume: Page:
ISSN:
Container-title:27th International Conference on Intelligent User Interfaces
language:
Short-container-title:

Author:

Guo Lijie¹,Daly Elizabeth M.²,Alkan Oznur²,Mattetti Massimiliano³,Cornec Owen²,Knijnenburg Bart¹

Affiliation:

1. School of Computing, Clemson University, United States and School of Computing, Clemson University, United States

2. IBM Research, Ireland and IBM Research, Ireland

3. Microsoft, Ireland and Microsoft, Ireland

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3490099.3511111

Reference54 articles.

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2. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

3. Öznur Alkan , Dennis Wei , Massimiliano Matteti , Rahul Nair , Elizabeth M Daly , and Diptikalyan Saha . 2022 . FROTE: Feedback Rule-Driven Oversampling for Editing Models. arXiv preprint arXiv:2201.01070(2022). Öznur Alkan, Dennis Wei, Massimiliano Matteti, Rahul Nair, Elizabeth M Daly, and Diptikalyan Saha. 2022. FROTE: Feedback Rule-Driven Oversampling for Editing Models. arXiv preprint arXiv:2201.01070(2022).

4. Power to the People: The Role of Humans in Interactive Machine Learning

5. Nanci Bell and Phyllis Lindamood . 1991. Visualizing and verbalizing: For language comprehension and thinking . Academy of Reading Publications Paso Robles , CA. Nanci Bell and Phyllis Lindamood. 1991. Visualizing and verbalizing: For language comprehension and thinking. Academy of Reading Publications Paso Robles, CA.

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