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

1. HOW TO MAKE A
LAWYERBOT THAT
REVIEWS NDAs
Marco Caspers
Legal and Technical Consultant at JuriBlox
LEGAL AUTOMATION

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

12. 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

15. 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

16. SENTENCE EXTRACTION
& CLASSIFICATION

17. SENTENCE
CLASSIFICATION

18. 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

19. 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

20. 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

21. (Not found - boilerplate)

23. (Not found - boilerplate)
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26. (Not found - boilerplate)

28. AprilMarchFebJanDecNovOctSep
USER GROWTH

29. Source collection
Data preparation
Feature selection
Algorithm selection
Training & testing
MACHINE LEARNING
IN 5 STEPS

30. Source collection
Data preparation
Feature selection
Algorithm selection
Training & testing
MACHINE LEARNING
IN 5 STEPS

31. 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)

32. 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