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DutchMLSchool. Machine Learning for Law

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Machine Learning for Law - ML for Executives Course.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.

Published in: Data & Analytics
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DutchMLSchool. Machine Learning for Law

  1. 1. HOW TO MAKE A LAWYERBOT THAT REVIEWS NDAs Marco Caspers Legal and Technical Consultant at JuriBlox LEGAL AUTOMATION
  2. 2. TODAY’S PROGRAM Case of NDA Lynn to learn about machine learning benefits in legal practice • Introduction of NDA Lynn • How does NDA Lynn make decisions? • Making NDA Lynn: machine learning • Takeaways
  3. 3. In-house use of NDA Lynn. Organizations are in control of the parameters that decide whether their employees should sign an NDA. ENTERPRISE FIELDS OF LAW • IT • Privacy • Labour • Corporate • Tenancy
  4. 4. Extract sentences Classify sentences by topic Create clauses from sentences on same topic Classify impact of clauses Create advice based on impact and position AUTOMATED NDAADVICE IN 5 STEPS
  5. 5. SENTENCE EXTRACTION & CLASSIFICATION
  6. 6. SENTENCE CLASSIFICATION
  7. 7. Security Recipient will maintain … Recipient shall ensure … Need to know Not disclose the Confidential … Recipient is authorized to … Notification duty Recipient agrees to promptly… Purpose limitation Use the Confidential … CLAUSE CREATION
  8. 8. Security Recipient will maintain … Recipient shall ensure … Purpose limitation Use the Confidential … Need to know Not disclose the Confidential … Recipient is authorized to … Notification duty Recipient agrees to promptly… Strict Limited Standard Relaxed Broad Limited Broad Standard Broad CLAUSE CLASSIFICATION
  9. 9. MutualGetGive Security Recipient will maintain … Recipient shall ensure … Need to know Not disclose the Confidential … Recipient is authorized to … Notification duty Recipient agrees to promptly… REPORT CREATION
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  14. 14. AprilMarchFebJanDecNovOctSep USER GROWTH
  15. 15. Source collection Data preparation Feature selection Algorithm selection Training & testing MACHINE LEARNING IN 5 STEPS
  16. 16. Source collection Data preparation Feature selection Algorithm selection Training & testing MACHINE LEARNING IN 5 STEPS
  17. 17. IMPORTANT CONSIDERATIONS • Investments vs gains • Who is training • Algorithm not biased • Annotator: knowledge, experience • Weakest link • Domain • More subjective, less suitable • Data sources • > 1000 for accuracy • Representative sample • Output must be valuable • Anyone paying for it? • Saving time/money on in-house or external lawyers? • No revenue from NDA, but high costs • Risky contracts (e.g. NDA, SLA) vs ‘irrelevant’ (privacy policy)
  18. 18. LEGAL AUTOMATION JuriBlox B.V. Jollemanhof 8a 1019 GW Amsterdam +31 20 229 33 45 info@juriblox.com juriblox.nlMarco Caspers m.caspers@juriblox.nl

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