Scalable and explainable legal prediction

Author:

Branting L. KarlORCID,Pfeifer Craig,Brown Bradford,Ferro Lisa,Aberdeen John,Weiss Brandy,Pfaff Mark,Liao Bill

Publisher

Springer Science and Business Media LLC

Subject

Law,Artificial Intelligence

Reference55 articles.

1. Al-Abdulkarim L, Atkinson K, Bench-Capon TJM, Whittle S, Williams R, Wolfenden C (2017) Noise induced hearing loss: an application of the angelic methodology. In: Legal knowledge and information systems—JURIX 2017: the thirtieth annual conference, Luxembourg, 13–15 December 2017, pp 79–88

2. Alarie B, Niblett A, Yoon A (2017) Using machine learning to predict outcomes in tax law. Available at SSRN https://ssrn.com/abstract=2855977 or https://doi.org/10.2139/ssrn.2855977

3. Aletras N, Tsarapatsanis D, Preotiuc-Pietro D, Lampos V (2016) Predicting judicial decisions of the European Court of Human Rights: a natural language processing perspective. PeerJ CompSci. https://peerj.com/articles/cs-93/

4. Aleven VAWMM (1997) Teaching case-based argumentation through a model and examples. PhD thesis, University of Pittsburgh, Pittsburgh. AAI9821228

5. Aleven V, Ashley K (1996) Doing things with factors. In: Proceedings of the 3rd European workshop on case-based reasoning (EWCR-96), Lausanne, pp 76–90

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