EY Aleph: Deep Learning Applied to Jurimetrics Practice
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The jurimetrics practice in Brazil, when exists, usually is done with local data from legal offices using data collected from its own cases maintained using time-consuming manual processes. Our approach is based on data collection of all public data (not only cases that we work) combined with our workforce of lawyers to create trained datasets in order to use in natural language processing deep learning models for attributes extraction, such as judge, lawyer, plaintiff, defendant, requests, and jurisprudence. The organization of unstructured data gathered enabled EY to deliver unprecedented analysis of litigation amounts deposits aiming to drastically reduce the provisions, besides other benefits in legal chain, especially in labor law.
EY Aleph: Deep Learning Applied to Jurimetrics Practice
- 1. EY ALEPH deep learning applied to jurimetrics papis | june 2018
- 3. relevance 283,4 76,9 39,1 Total lawsuits in the balance sheets (R$ Billions)1 Tax Civil Labor 32% tax litigation in relation to the market value1 ICMS 90,4 Social Contribution on Net Income 65,2 Income Tax 22,8 PIS/Cofins 15,57 CIDE 11,72 Top Litigation Taxes (R$ Billions)1 1Source: ¡°O contencioso tribut¨¢rio sob a perspectiva corporativa¡±, Ana Teresa L R Lopes All related data is about 2014 regarding 30 top companies in Brazil
- 4. real jurimetrics for labor law legal provisions deal & defense models deal pricing operation optimization law firms performance
- 5. regional labor tribunal 204 courts in region 2 (S?o Paulo) universal data physical lawsuits over than 7 MM of documents (pdf) available electronic lawsuits all lawsuits from 2015 more structured (html)
- 6. our hacking skills automatic scraping: not human mimic captcha solving
- 7. sometimes is not easy, even for pros but we can do little tricks¡
- 8. 80% data prep cv 20% + cnns
- 9. putting all together public data collections normalization attribute extractions audit visualization scraping rpa deep learning computer vision document conversions unrtf poppler regex machine learning human check support web app web app viztools
- 10. challenge large-scale processing of files constant evolution accessibility speed
- 11. rate OUR setup stack defined by ia programing language & azure public cloud low cost
- 12. job management back-end app rest api front-end app storage queue no-sql sql gitci table
- 14. cosmosdb no friction with data structure alterations, but not good with dataviz tools for BI teams nor application stack
- 15. mysql perfect to dataviz tools and support the app summary from no-sql database, read-only scenario
- 16. job management
- 17. ops redis as queue management
- 18. load
- 19. blob queue table stores objects with possibility of local redundancy (3 copies) or global (6 copies) has local redundancy (3 copies of the message) messages expire in 7 days storage of key-value type "No-sql like" does not allow map- reduce operations filters only by key (recommended) raw documents cleaned documents ml models job management orchestration configurations
- 21. slack everywhere
- 22. ai solution in a box cosmos no-sql app insights sql aleph admin ruby functions queue blob tables jenkinstfs celery users api mgnt redis cache cloud for b2b customers ey aleph ruby + ember mechanical turk staff
- 23. check ai black boxes but carefully
- 24. our open boxes
- 25. In 20/02/2017 was declared ¡ 20/02/2017 In 20 of December of 2017 ¡ 20/12/2017 In the second day of January of two thousand and seventeen ¡ 02/01/2017 In eleventh day of March of 2016 ¡ 01/03/2016 our challenge: real unstructured data¡
- 26. ¡. Foundation Extra Hours Worker claims that the hours after work were not ¡. D E C I S I O N Of the additional of unhealthiness. The author worked for the claimed ones ¡ II ¨C FOUNDATION - Rescission sums The author postulates the payment of the amounts resulting from the unmotivated waiver ¡ J u d g e m e n t ¡ moral damages. The requester claimed that during his work at ¡ ¡and it gets worse REQUESTS CONCLUSION
- 29. TYPE OF PHRASE ? Requests ? Sentences ? Other DECISION ? Granted ? Overruled 1 2 classifiers
- 30. Text Representation Tokenizers Tested Algorithms WORD COUNT TF-IDF N-GRAMS + STOPWORDS UNIGRAM + STEMMING + STOPWORDS UNIGRAM + STOPWORDS REGRESS?O LOG?STICA RANDOM FOREST GRADIENT BOOSTING CLASSIFIER MULTINOMIAL NA?VE BAYES STOCHASTIC GRADIENT DESCENT SUPPORT VECTOR CLASSIFIER traditional nlp approaches
- 31. traditional nlp approaches text representation tokenizer classifiers algorithms WORD COUNT TF-IDF N-GRAMS + STOPWORDS UNIGRAM + STEMMING + STOPWORDS UNIGRAM + STOPWORDS LOGISTIC REGRESSION RANDOM FOREST GRADIENT BOOSTING CLASSIFIER MULTINOMIAL NA?VE BAYES STOCHASTIC GRADIENT DESCENT SUPPORT VECTOR CLASSIFIER
- 32. Treinamento Teste first results logistic regression + tf-idf + no stopwords + stemming training testing f1-score: 0,90 f1-score: 0,81
- 34. TYPE OF PHRASE ? Demands ? Sentences ? Nothing DECISION ? Granted ? Overruled 1 2 DEEP LEARNING TRADITIONAL NLP classifiers
- 35. 1DConvNet LSTM Bidirectional-LSTM Task Specific Pre-trained (Word2Vec) EMBEDDINGS (Words) LAYERS (Phrases) LOSS FUNCTION SGD RMSProp Nadam
- 36. embeddings pre trained f-score = 0,897
- 37. embeddings task specific f-score = 0,896
- 38. ¡°I do not find the defendant¨C in light of all available evidence and according to the law and the decision of the jury, and so it goes and yadda yadda ¨C to be guilty.¡± recurrent neural networks
- 39. RNN Recurrent Neural Network LSTM Long-Short Term Memory
- 42. stop guessing