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Machine learning in predictive analytics on judicial decision-making
Legal professionals globally are under pressure to provide ‘more for less' – not an easy challenge in the era of big data, increasingly complex regulatory and legislative frameworks and volatile financial markets. Although largely limited to information retrieval and extraction, Machine Learning app...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2021
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| _version_ | 1867614018355593216 |
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| access_status_str | Open Access |
| author | Pienaar, Celia |
| author2 | Nitschke, Geoff Stuart |
| author_browse | Nitschke, Geoff Stuart Pienaar, Celia |
| author_facet | Nitschke, Geoff Stuart Pienaar, Celia |
| author_sort | Pienaar, Celia |
| collection | Thesis |
| description | Legal professionals globally are under pressure to provide ‘more for less' – not an easy challenge in the era of big data, increasingly complex regulatory and legislative frameworks and volatile financial markets. Although largely limited to information retrieval and extraction, Machine Learning applications targeted at the legal domain have to some extent become mainstream. The startup market is rife with legal technology providers with many major law firms encouraging research and development through formal legal technology incubator programs. Experienced legal professionals are expected to become technologically astute as part of their response to the ‘more for less' challenge, while legal professionals on track to enter the legal services industry are encouraged to broaden their skill sets beyond a traditional law degree. Predictive analytics applied to judicial decision-making raise interesting discussions around potential benefits to the general public, over-burdened judicial systems and legal professionals respectively. It is also associated with limitations and challenges around manual input required (in the absence of automatic extraction and prediction) and domain-specific application. While there is no ‘one size fits all' solution when considering predictive analytics across legal domains or different countries' legal systems, this dissertation aims to provide an overview of Machine Learning techniques which could be applied in further research, to start unlocking the benefits associated with predictive analytics on a greater (and hopefully local) scale. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/33925 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:45:22.266Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/33925 Machine learning in predictive analytics on judicial decision-making Pienaar, Celia Nitschke, Geoff Stuart Computer Science Legal professionals globally are under pressure to provide ‘more for less' – not an easy challenge in the era of big data, increasingly complex regulatory and legislative frameworks and volatile financial markets. Although largely limited to information retrieval and extraction, Machine Learning applications targeted at the legal domain have to some extent become mainstream. The startup market is rife with legal technology providers with many major law firms encouraging research and development through formal legal technology incubator programs. Experienced legal professionals are expected to become technologically astute as part of their response to the ‘more for less' challenge, while legal professionals on track to enter the legal services industry are encouraged to broaden their skill sets beyond a traditional law degree. Predictive analytics applied to judicial decision-making raise interesting discussions around potential benefits to the general public, over-burdened judicial systems and legal professionals respectively. It is also associated with limitations and challenges around manual input required (in the absence of automatic extraction and prediction) and domain-specific application. While there is no ‘one size fits all' solution when considering predictive analytics across legal domains or different countries' legal systems, this dissertation aims to provide an overview of Machine Learning techniques which could be applied in further research, to start unlocking the benefits associated with predictive analytics on a greater (and hopefully local) scale. 2021-09-15T15:24:07Z 2021-09-15T15:24:07Z 2021 2021-09-15T02:21:00Z Master Thesis Masters MPhil http://hdl.handle.net/11427/33925 eng application/pdf Department of Computer Science Faculty of Science |
| spellingShingle | Computer Science Pienaar, Celia Machine learning in predictive analytics on judicial decision-making |
| thesis_degree_str | Master's |
| title | Machine learning in predictive analytics on judicial decision-making |
| title_full | Machine learning in predictive analytics on judicial decision-making |
| title_fullStr | Machine learning in predictive analytics on judicial decision-making |
| title_full_unstemmed | Machine learning in predictive analytics on judicial decision-making |
| title_short | Machine learning in predictive analytics on judicial decision-making |
| title_sort | machine learning in predictive analytics on judicial decision making |
| topic | Computer Science |
| url | http://hdl.handle.net/11427/33925 |
| work_keys_str_mv | AT pienaarcelia machinelearninginpredictiveanalyticsonjudicialdecisionmaking |