This presentation analyzes the role that Machine Learning plays in legal automation with a real-world Machine Learning application. Speaker: Arnoud Engelfriet, Co-Founder at Lynn Legal. *ML in GRC 2021: Virtual Conference.

1. MACHINE LEARNING IN LEGAL AUTOMATION:
HOW TO TRUST A LAWYERBOT
ARNOUD ENGELFRIET, CO FOUNDER AT LYNN LEGAL

3. 66%
of respondents would
consent to an
autonomous AI-driven
system to perform
invasive surgery.

5. 36%
of respondents would
trust an autonomous
AI-driven police dog to
intervene appropriately
…

6. 36%
… but only if it
wears a visible
muzzle.

8. 23%
of respondents would
file a tax return
created by an AI
system without a
human final check.

10. 79%
of respondents would
trust AI- flowerpot
Tess as the sole
supervisor of an
elderly person in a
care home.

15. 48%
of respondents would
send a document
reviewed by lawyerbot
Lynn to its
counterpart without
human review.

16. 88%
of respondents would
use a lawyerbot as a
quickscan to decide
whether or not to
proceed at all.

17. • Reads and understands
legal clauses.
• Intuitive grasp of impact
• Chooses her battles
• Applies statistics & similarity
to classify clauses
• Works strictly according to
predefined playbook
• Raises every issue,
every time

19. Pre-check: Give users a
feeling of what the human
lawyer will say
Quickscan: If lawyerbot
says OK, it’s ok
Otherwise send it to the
human lawyer
Fast lane: If value is low
then let lawyerbot review it
Otherwise send it to the
human lawyer
Preliminary scan: First
resolve the issues raised by
the lawyerbot. Then send it
to the human lawyer.

20. •Change: high
•Risk reduction: high
•Cost savings: medium
•Change: high
•Risk reduction: high
•Cost savings: high
•Change: low
•Risk reduction: medium
•Cost savings: medium
•Change: minimal
•Risk reduction: low
•Cost savings: low
Precheck Quickscan
Preliminary
scan
Fast lane

21. THANK YOU!
Arnoud Engelfriet
Co founder at Lynn Legal
A.Engelfriet@juriblox.nl
+31 (0)20 229 33 45
Lynnlegal.AI