Towards Explainable AI: Assessing the Usefulness and Impact of Added Explainability Features in Legal Document Summarization-Reference-Cited by-同舟云学术

Towards Explainable AI: Assessing the Usefulness and Impact of Added Explainability Features in Legal Document Summarization

Published:2021-05-08 Issue: Volume: Page:
ISSN:
Container-title:Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
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Author:

Norkute Milda¹,Herger Nadja¹,Michalak Leszek¹,Mulder Andrew²,Gao Sally²

Affiliation:

1. Thomson Reuters Labs, Switzerland

2. Thomson Reuters Labs, United States

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3411763.3443441

Reference14 articles.

1. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

2. Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural Machine Translation by Jointly Learning to Align and Translate. arXiv:1409.0473. Retrieved from: https://arxiv.org/abs/1409.0473 Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural Machine Translation by Jointly Learning to Align and Translate. arXiv:1409.0473. Retrieved from: https://arxiv.org/abs/1409.0473

3. Using thematic analysis in psychology

4. Bryce Goodman and Seth Flaxman. 2016. European Union regulations on algorithmic decision-making and a “right to explanation.” DOI: https://doi.org/10.1609/aimag.v38i3.2741 Bryce Goodman and Seth Flaxman. 2016. European Union regulations on algorithmic decision-making and a “right to explanation.” DOI: https://doi.org/10.1609/aimag.v38i3.2741

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