Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective
- Submitting institution
-
The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2578
- Type
- D - Journal article
- DOI
-
10.7717/peerj-cs.93
- Title of journal
- PeerJ Computer Science
- Article number
- e93
- First page
- -
- Volume
- 2
- Issue
- -
- ISSN
- 2376-5992
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
D - Natural Language Processing
- Citation count
- 85
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The first paper to use NLP techniques to develop models to predict judicial outcomes using solely textual information describing the facts of a case. It has had widespread academic and industry impact; receiving worldwide media coverage (http://www.nikosaletras.com/press.html) and acknowledgement by the British Supreme Court President (https://www.supremecourt.uk/docs/speech-170703.pdf). The work has resulted in invitations to contribute to policy reports by the European Council (https://rm.coe.int/ethical-charter-en-for-publication-4-december-2018/16808f699c), and the Law Society of England and Wales (https://www.lawsociety.org.uk/policy-campaigns/documents/technology-and-the-law-commission-session-1-evidence-summary/), led to a follow-up paper (Chalkidis et al., ACL 2019),organisation of the Natural Legal Language Processing workshop (http://nllpw.org/) and 15 invited talks worldwide - including the EU Commission.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -