Conference item
Topic modelling for risk identification in Data Protection Act Judgements
- Abstract:
- Data protection legislation, such as the EU’s General Data Protection Regulation (GDPR), obliges data controllers to address risks to personal data. Risk assessment rules for data protection stipulate taking into account instances where the processing of personal data may affect other rights of the individual. It is acknowledged that engineering systems in order to address all risks is challenging and there is a need for prioritisation of risks. Previously decided decisions regarding personal data may provide insight to facilitate this. To this end, we ask: (i) in what context has data protection legislation been invoked in courts? and (ii) what other legal concerns were affected by these cases? To answer these questions, we use structural topic modelling (STM) to extract topics from the case judgements related to the United Kingdom’s Data Protection Act, incorporating covariate information related to the case outcomes, such as court type and year. The outputs of the model can be utilised to provide topics which relate to context; they can also examine how the other associated variables relate to the resultant topics. We demonstrate the utility of unsupervised text clustering for context and risk identification in legal texts. In our application, we find that STM provides clear topics and allows for the analysis of trends regarding the topics, clearly showing where data protection issues succeed and fail in courts.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 265.4KB, Terms of use)
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- Publisher copy:
- 10.1007/978-3-031-36190-6_5
Authors
- Publisher:
- Springer
- Host title:
- Proceedings of the 15th International Workshop on Juris-informatics
- Volume:
- 13856
- Pages:
- 62–76
- Series:
- Lecture Notes in Artificial Intelligence
- Publication date:
- 2023-07-19
- Acceptance date:
- 2021-10-06
- Event title:
- 15th International Workshop on Juris-informatics
- Event location:
- Keio University Kanagawa, Japan
- Event website:
- https://www.kl.itc.nagoya-u.ac.jp/jurisin2021/
- Event start date:
- 2021-11-14
- Event end date:
- 2021-11-15
- DOI:
- EISSN:
-
1611-3349
- Language:
-
English
- Keywords:
- Pubs id:
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1204172
- Local pid:
-
pubs:1204172
- Deposit date:
-
2021-10-19
Terms of use
- Copyright holder:
- Springer Nature
- Copyright date:
- 2023
- Rights statement:
- © 2023 Springer Nature Switzerland AG
- Notes:
- This paper will be presented at the 15th International Workshop on Juris-informatics (JURISIN 2021), Keio University Kanagawa, Japan, 14th-15th November 2021. This is the accepted manuscript version of the article. The final version is available online from Springer at: https://doi.org/10.1007/978-3-031-36190-6_5
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